2021-12-19, current version: 1.0 None

Corresponding author: Chenggui Sun (e-mail<EMAIL_ADDRESS>and Li Bin
<EMAIL_ADDRESS>
# Product Re-identification System in Fully Automated Defect Detection

Chenggui Sun and Li Bin Song  CHENGGUI SUN1 AND LI BIN SONG2  Department of
Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E8
(e-mail<EMAIL_ADDRESS> Department of Computing Science, University of
Alberta, Edmonton, Alberta, Canada T6G 2E8 (e-mail<EMAIL_ADDRESS>
###### Abstract

In this work, we introduce a method and present an improved neural work to
perform product re-identification, which is an essential core function of a
fully automated product defect detection system. Our method is based on
feature distance. It’s the combination of feature extraction neural networks,
such as VGG16, AlexNet, with an image search engine - Vearch. The dataset that
we used to develop product re-identification systems is a water-bottle dataset
that consists of 400 images of 18 types of water bottles. This is a small
dataset, which was the biggest challenge of our work. However, the combination
of neural networks with Vearch shows potential to tackle the product re-
identification problems. Especially, our new neural network - AlphaAlexNet
that a neural network was improved based on AlexNet could improve the
production identification accuracy by four percent. This indicates that an
ideal production identification accuracy could be achieved when efficient
feature extraction methods could be introduced and redesigned for image
feature extractions of nearly identical products. In order to solve the
biggest challenges caused by the small size of the dataset and the difficult
nature of identifying productions that have little differences from each
other. In our future work, we propose a new roadmap to tackle nearly-identical
production identifications: to introduce or develop new algorithms that need
very few images to train themselves.

###### Index Terms:

product re-identification, feature extraction, image search, image similarity

=-15pt

##  I Introduction

An Image search/retrieval system is a software system to find similar images
to a query image from a set of image databases. Such systems have been widely
used and studied in E-commerce, fashion industries, and person re-
identification [1, 2, 3, 4]. Image search is a query-based image similarity
matching technique. This technique is evaluated by three major metrics:
accuracy, performance on responding time, and scalability. An image search
system has two major sub-processes: query processes and indexing processes. In
the query process, an image is given to the system and the system returns one
or multiple images that are similar to the query image. The indexing process
is usually a batch process. It loads the features of multiple pre-existing
images to the database. By combining the query processes and indexing
processes, an image search system can return similar images as a response to a
user’s request by extracting images’ features using images’ index numbers.
Figure I demonstrated a generalized image search system based on queries [5].
As it shows, images are preprocessed for further analysis at first. Then,
feature vectors are extracted, and when it is necessary, dimension reductions
are performed via various steps at this step. The obtained indexes of images
are applied to perform feature matching by measuring the similarity distances
between the query image and the store images in the dataset. In the end, the
image(s) that matches the query image are displayed [6].

[t!](topskip=0pt, botskip=0pt, midskip=0pt)fig1.png Scheme of Image Search
Process

Image features are generally the visual descriptor of images to humans and
include color, shape, text, and face features [7]. Color is one of the most
used visual features of images. Color images are typically represented by RGB
(Red, Green, Blue) space. The distributions of colors in RGB space in images
can be represented by histograms, in which one histogram is assigned to each
pixel value. The quantities of histograms’ bins determine the color
quantization. The spatial relations of pixels should also be used in
conjunction with color features. Shape feature includes edges, contours, which
can be achieved by various methods. Image texture refers to the visual
patterns, which have properties of homogeneity or arrangement of colors and
their intensity. Structural and statistical representation methods have been
used for texture representation in computer vision.

The query subprocess of the image search isn’t the research focus of this
project. Our project requires us to develop a cold-started fully automated
defect detection system to identify a product as a new category or existing
category. If it’s a new category, the images of products will be collected and
processed to train a model to detect defects. If it’s an existing category,
then an existing category-based model will be used to perform defect
detection. The challenges of this project include:

  * •

Small dataset: 400 images of 18 types of bottles;

  * •

Simple geometric properties of products;

  * •

Un-consistent colors due to non-ideal illumination conditions of the factory.

As the sample images of water bottles in Figure I show, the simple shape and
un-consistent colors of water bottles may lead to low accuracy of image
similarity search to identify nearly identical water bottles.

[t!](topskip=0pt, botskip=0pt, midskip=0pt)fig2.png Six of the bottles for
product re-identification algorithm development

In this work, we tested the performance of classic AlexNet classification to
identify bottles’ type as different classes and introduce a feature distance-
based method, the combination of image feature extraction using neural
networks and image search using an image search engine - Vearch. The main
contributions of this work are:

  * •

Created a new dataset that consists of 400 images of 18 types of water bottles
from monitoring videos collected from real-world production assembly lines;

  * •

Examined the performance of AlexNet on object identifications using classic
classification method when each type of water bottle was treated as one class;

  * •

Introduced a feature extraction and feature-distance-based image search method
to perform product identification. The features were extracted using pre-
trained VGG16 and AlexNet, then feed to image search engine - Vearch to
perform image search;

  * •

Introduced our new AlphaAlexNet for feature extraction. AlphaAlexNet is an
improved model with more channels that was developed on top of AlexNet. The
deployment of AlphaAlexNet could improve the image search accuracy of Vearch
by four percent than the image search accuracy of the combination of Vearch
and AlexNet.

##  II Literature Review

For a fully automated real-time production recognition system, the main tasks
are to extract image frames, remove background and segment objects, build an
image search/retrieval engine and extract features. There are many excellent
articles in background removal, object segmentation, image search engine and
image feature extractions for applications in facial recognition, gesture
recognition, medical, fashion, e-commerce and manufacturing industries [8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53, 54]. In this review, we will introduce and discuss the
progress and some existing research in these areas.

### Background Removal and Image Retrieval

One of the challenges is that an image background is complex, and some colors
are very close. In order to reduce the impact on the later step of image
processing, there are a few options we can do about it. One option is to do
background removal before image processing (e.g., image search and image
indexing). Many pieces of research have been done on image background removal.
One of the approaches is to do background subtraction based on multiple
frames. Because each frame has a similar background and foreground object, the
subtraction can be done. And it is used to remove the background. Zhao et al.
[28] proposed a new Arithmetic Distribution Neural Network (ADNN), which can
be trained for background removal tasks. This pager also analyzes traditional
background removal approaches such as using the Gaussian mixture model,
unsupervised Bayesian classifier. These approaches have significant
limitations. Because of the complexity and diversity of all potential images,
these approaches could not generate a perfect classification of pixels for
background removal tasks. Other methods are built on top of Deep Learning
Neural Networks, and recent research and work have been done to improve the
result. The paper proposed ADNN is one of the approaches. The ADNN
architecture uses the distributed neural network for background subtraction.
The input is histograms of the current image and referenced histograms as
counterparts from the referenced image. These inputs are passed into the
arithmetic distribution layers. And a classification NN is then attached for
the classification of background and foreground for pixels labels. Based on
the paper, the ADNN shows promised performance and good accuracy and can be
applied in real-life applications.

Qin et al. proposed a new network for object detection [27]. While once the
object(foreground) is detected, the attractive object can be taken out from
the background. In the paper, the team designed a deep neural network called
U2-Net for object detection. The research analyzes the existing popular NN
used as Salient Object Detection (SOD), such as AlexNet, VGG, ResNet,
DenseNet, etc. The study reveals the limitation of these NN that all these
networks are originally designed for classification. The features extracted
include both foreground objects and also background information. While it is
good enough for classification but there is also a limitation for saliency
detection. Another boundary from the research shows that these deep network
models require higher computer RAM and compute power to run, limiting the use
cases. For this reason, one of the techniques is to reduce input image
resolution in order to reduce RAM and compute cost. This limits the output
resolution. The research team designed a new network called U2-Net. The U2-Net
maintains higher image quality with low RAM usage and low CPU compute costs.
The critical design of U2-Net is a two-level nested U-structure without using
any pre-trained NN classification models. The U2-Net can be trained from
scratch based on use cases without depending ImageNet. The U2-Net can go
deeper(nested) for processing high resolution without generating extreme RAM
usage and computation costs. The U2-Net can be as small as 4.7MB and up to
176.3 MB with good results against the SOTA models at 40 FPS. The U2-Net team
also open source their implementation code. This code is indeed a significant
contribution to this subject. A bench of real-life applications has been made
on the top of U2-Net. Such as background removal app, portrait generation,
etc. The U2-Net has an enormous potential that can help us with image
background removal tasks.

Once we have a good image input, the next step is to identify the bottle from
the image. Because of the client’s requirements, any supervised learning model
does not fit the needs. We are looking for a solution that can identify the
bottle either is new or existing being processed before. The reason behind
this is that the factory can produce a new type of bottle at any time. The
system must be able to adapt to the new bottle and prepare the system for
defect detection. In the object identification research area, a lot of work
has been done on person identification and fashion recommendation systems. The
work on those areas could not apply to this project directly because the
bottle’s image is so similar and has fewer features than human and fashion
wearing. But the approaches are very inspiring. Most of the methods are built
on top of Convolutional Neural Networks (CNNs). But in recent years, the
transformers technique has been widely used in the Nature Language Processing
field and gained significant success. Vision Transformer (ViT) starts to be
used in image classification and identification fields. Wang et al. [26]
proposed a new Pyramid Vision Transformer (PVT) method and overcame some ViT
low-resolution output because of costs on computation and RAM. In their
experiment, the PVT method shows promising results on object detection and
semantic and instance segmentation tasks with better performance. Sharma et
al. highlight their work on top of these ViT and PVT researches [25]. They
proposed a Locally Aware Transformer (LA-Transformer) method and used it on
person re-identification (Re-ID). The person Re-ID problem is different from
the classification problem. It needs to detect a person and also be able to
identify the same person from images coming from various sources (e.g.,
Security Cameras have no relation to each other). The work done by this
research gives excellent results on high performance and high accuracy on
person Re-ID problem. Person Re-ID research result inspires our project, that
LA-Transformer can potentially apply on the issue of fewer features product
Re-ID problem.

Once we have a method to capture the object(bottle)’s features, we need a
quick way to search the image from the existing data (bottles already known).
This work involves two pieces, indexing and matching. In our project use
cases, there is a crucial requirement of real-time processing. This requires
the solution to be able to run at low latency with high accuracy. Facebook AI
team does the recent successful research and created a library(faiss) based on
the study (code on github.https://github.com/facebookresearch/faiss) [24].
They proposed combining graph traversal and compact representations to
accomplish the similarity search tasks. This approach is proven to use less
memory and lower computation costs. In a single machine, it can handle
billions of image indexing and searching. Because this study is so successful
that JD.com had another research to apply this method to E-commerce [23].
JD.com use image search to provide similar production recommendations on top
of over 100 billion product images. In JD.com’s study, they proposed a high-
efficiency, scalable distributed system for efficient similarity search
architecture. They also open source the implementation of the research
code(https://github.com/vearch/vearch). The code can be either reused or
modified to fit new use cases. The work from JD.com is a big help for image
similarity search. Their code can be reused for image indexing.

Jeff Johnson et al. [55] proposed a design of better-utilizing GPUs to perform
similarity searches. In their paper, they proposed not only a k-selection
algorithm that can operate in fast register memory and be fusible with other
kernels as a result of its flexibility but also a near-optimal algorithmic
layout for exact and approximate k-nearest neighbor search. They applied their
design for brute-force, approximate and compressed-domain search based on
product quantization in different similarity search scenarios. Their algorithm
and algorithmic layout enable better utilization of GPU computation, which
outperformed previous art by a large margin. F. Li et al. [4] introduced a
solution for e-commerce platforms to classify and search productions based on
images. They trained neural networks to conduct image classification by using
transfer learning with pre-trained models such as VGG19 and using autoencoders
and cosine similarity to search similar images

### Feature Extraction

There are many approaches to extract features, including low-level local
features (such as edges and corners), and global features [56]. This
literature review will focus on review works that utilized deep learning
based-methods to extract image features. Deep learning is essentially a neural
network with three or more layers [57], which can be stacked. The output of
the previous layer is the input of the next layer, and these layers act as
feature extractors. A deep learning model is an end-to-end learning model, its
fully trained layers can perform much better feature extractions[58]. The most
popular deep learning networks that have been used in feature extraction are
AlexNet, VGGNet, ResNet and U-net [47, 48, 49], [51].

AlexNet consists of three types of layers: five convolutional layers, three
fully-connected layers with a final 1000-way softmax, max-pooling layers that
follow some of the convolutional layers. Its 60 million parameters and 650,000
neurons make overfitting become a concern. In order to reduce overfitting in
the fully-connected layers, AlexNet employed “dropout” as regularization [49].
Lv et al. [35] proposed DMS-Robust AlexNet, a neural network designed based on
AlexNet architecture, to recognize maize leaf disease. Their network combined
dilated convolution and multi-scale convolution to improve its capability to
extract features, used batch normalization to prevent over-fitting, and PRelu
activation function and Adabound optimizer to improve both convergence and
accuracy. Wang and Han [32] proposed a modified AlexNet convolutional neural
network for face feature point detection. This modified network has 4
convolution layers and 3 fully connected layers. It removes one layer of
convolutional layers of traditional networks and adds the Batch-Normalization
layers. The model’s input is three sub-images of a face image, which is
divided by the model, overlap with each other and have a color channel of
their own. The model output the coordinates of the face feature points. Yuan
and Zhang [31] used AlexNet and Caffer framework to extract features for image
retrieval. The Inria Holidays and Oxford Buildings datasets were used for
their experiments, which revealed that the fusion feature could contribute to
the improvement of image retrieval. Besides those works that we discussed
above, there are many AlexNet’s applications in extracting features for hand
gestures, remote sensing images, image registration and bearing fault
diagnosis as well [33, 34, 36, 37, 38].

VGGNet addresses the very important aspect of ConvNet architecture design -
depth. It pushes the depth of the network to 16-19 weight layers by adding
more (3 × 3) convolution filters in all layers, resulting in significantly
more accurate ConvNet architectures [51]. Khaireddin1 and Chen [42] adopted
the VGGNet architecture to perform facial emotion recognition on the FER2013
dataset. Their network has 4 convolutional stages that consist of two
convolutional blocks and a max-pooling layer individually, and 3 fully
connected layers. The convolutional stages extract features for training the
fully connected layers to classify the inputs. Their model achieves state-of-
the-art single-network accuracy of 73.28% on FER2013 by tuning the model and
its hyperparameters. Zhou et ai. [41] proposed to combine different
granularity features from the block1, block2, block3, block4, and block5 in
VGG to construct a new network architecture. A local fully connected layer was
added after each block to reduce the dimensionality of the features. The
combined five different granularity features are fed to the first of three
global fully connected layers as input. By doing this, the information flows
from a lower layer directly to a fully connected layer and the feature reuse
can be increased. Their network architecture also reduces the number of
parameters by removing some neurons in two global fully connected layers. This
architecture was examined by using CIFAR-10 and MNIST datasets and achieved
better performance than traditional VGGs. There are more works that use or
adapt VGGNet to extract features for various applications [39, 40, 58].

ResNet introduces a deep residual learning framework to address the
degradation problem and shortcut connections to simply perform identity
mapping, which adds neither extra parameter nor computational complexity. It’s
easy to optimize and gain the increases of accuracy from greatly increased
depth[48]. Corbishley et al. [46] utilized the ResNet-152 CNN with super-fine
attributes to recognize human attributes from surveillance video for person
re-identification. After re-annotating gender, age and ethnicity of images
from an amalgamation of 10 re-id datasets - PETA, their work performed
significantly better to retrieval images than conventional binary labels did:
a 11.2 and 14.8 percent mAP improvement for gender and age, further surpassed
by ethnicity. Kwok, S. [45] presented a framework that made use of Inception-
Resnet-v2 to classify breast cancer whole slide images into regions of: normal
tissue, benign lesion, in-situ carcinoma and invasive carcinoma. His work won
first place in ICIAR 2018 Grand Challenge on Breast Cancer Histology Images.
Chen et al. [44] proposed a modularized Dual Path Network (DPN), which
consists of Residual Network (ResNet) and Densely Convolutional Network
(DenseNet), for image classification. This DPN architecture can reuse and
explore new features with ResNet and DenseNet. It performed better than
DenseNet and ResNet alone on the ImagNet-1k dataset, the PASCAL VOC detection
dataset, and the PASCAL VOC. Huang et al. [43] used this ResNet-based DPN
architecture to extract features and classify breast cancer images and
achieved great results.

There are other great neural networks and modified networks based on these
architectures used for feature extraction, such as U-Net [47], GoogleNet [50]
and MobileNets [30]. U-Net [47] has 23 convolutional layers that can be
divided into a contracting stage for down-sampling and a symmetric expanding
stage for up-sampling. The contracting path has two unpadded 3x3 convolutions,
a ReLU and a 2x2 max pooling layer. The expanding path performs the up-
sampling of the feature map and reduces the number of feature channels by
half, and concatenates the corresponding feature map cropped from the
contracting path. After the expanding path, a 64-component extracted feature
vector is fed to the final 1x1 convolution layer for classifications.
GoogleNet [50] has 22 layers and its name refers to the incarnation of the
Inception module. MobileNets [30] are a class of models that can build
lightweight neural networks for applications in mobile and embedded vision
using depthwise separable convolutions. Its depthwise separable convolutions
factorize a standard convolution into a depthwise convolution that applies a
single filter to each input channel, and a 1x1 pointwise convolution that
combines the outputs. Besides convolutional neural networks, there are also
other networks based on Recurrent Neural Networks (RNN) and long short-term
memory (LSTM) for extracting deep learning features [58], which is not the
focus of this project.

The above networks and articles that we discuss mainly focus on extracting
features from images. However, the feature extraction of real-time videos is
essential for releasing more potentials for fully automated defect detections.
Shi et al. [29] presented the first convolutional neural network that can
perform real-time super-resolution of 1080p videos on a single GPU. The
feature maps are extracted in the low-resolution space, then upscaled to high-
resolution output by an efficient sub-pixel convolution layer. Their approach
was evaluated using publicly available datasets and performed significantly
better and faster than other existing CNN-based methods.

##  III Proposed Methods

In this project, two methods were used to perform bottle identification. Our
first method is to utilize classic AlexNet to perform four types of bottles
classifications to identify bottles. The first method is our baseline work.
Our second method is to employ the combination of Vearch and VGG16 and AlexNet
to perform the image similarity search.

### AlexNet

We applied transfer learning by using pre-trained AlexNet and resigning the
last layer to perform the four classes bottle classification. The architecture
of AlexNet contains five convolutional layers and three fully connected
layers. ReLU (Rectified Linear Unit) was used as the activation functions and
MaxPooling with a stride of three was used in the pooling layers. Dropout
layers were applied to reduce overfitting. The classifier consists of seven
layers: 1) two dropout layers; 2) each of the dropout layers is followed by
one fully connected layer and one ReLU layer; 3) the last layer is a fully
connected resigned layer with output to be set as the number of tested
classes.

### Feature Distance Based Image Similarity Search

Our feature-distance-based image similarity search works as follows:

  * •

Use neural networks to extract features;

  * •

Store extracted features in Vearch;

  * •

Send query/test images from dataset or cameras to feature extraction models;

  * •

Send extracted features to Vearch to perform an image search.

The core task of our works is to develop an efficient neural network for
feature extraction. The feature extraction model that we tested and developed
are listed as follows:

  * •

VGG16: the pre-trained VGG16 is used to extract features from our input image.

  * •

AlexNet: the pre-trained AlexNet is used to extract features from our input
image

  * •

AlphaAlexNet: six extra alpha channels are added to basic AlexNet to help the
model to put more weight on shapes and colors. The definition of alpha follows
the convention of the image alpha channels.

[t!](topskip=0pt, botskip=0pt, midskip=0pt)fig3.png The design layout of
bottle re-identification (query) system

Vearch, a visual search system developed by JD E-commerce Platform, is used as
our image search similarity engine. VGG16, AlexNet are two classic models for
object classifications. In this work, they are used to extract features.
AlphaAlexNet is integrated into Vearch search solution. After features are
extracted, they are submitted to Veach for image similarity matches.

Vearch consists of two subsystems to perform indexing and search [23].
Indexing is the core subsystem of Vearch and stores extracted image features
as image databases. In the original Vearch system, the indexes for all the
images are periodically built. The product update events, which include the
addition, deletion, and modification of a product image, can trigger Vearch to
update the indexes immediately. In our design, if a new image is detected, it
will be sent to the indexing process and added to the image database.

Another subsystem of Vearch is the search subsystem, which has three key
components: Blender, Broker, and Searcher. When a query from a user is
forwarded to one of the blenders. The blender will send the query to each
broker to contact a subset of searchers to search similar images from a
partition of the entire image set in parallel. The top k most similar images
will be sent back to the requesting broker, which combines the results from
its searchers then sends them to the blender. The combined results will be
ranked by the blender and be returned to the user. Vearch’s three-level
architecture makes it scalable to different levels of tasks of image indexing
and searches. The details of this architecture are shown in figure III.

[h!](topskip=0pt, botskip=0pt, midskip=0pt)fig4.png Architecture of Vearch
System[23]

##  IV Experiments

In this section, we introduce the dataset that we used to evaluate the
proposed methods and models.

We obtained 18 classes of water bottle images from production surveillance
videos of a factory with the support of our client - Zerobox Inc. In order to
reduce the complexity of experiments, 17 classes of bottles didn’t have logos
on them, except one class. Because our dataset has only about 400 images, we
also tried to test our models and methods with CIFAR10 and CIFAR100. Due to
the low computing performance of our computing power, these tests with
CIFAR100 weren’t completed. Because the size of the CIFAR10 is also small, the
test on CIFA10 did not produce any meaningful results that we were expecting.
Therefore, these results and implementations are not included here.

The images in figure V demonstrate the 18 types of water bottles used in our
research. These images only illustrate the colors and shape of the bottles,
not the ratio of their actual size for visual purposes. The production
backgrounds of these bottles have been removed in our pre-process stage.

| AlexNet2bottle | AlexNet4bottle | AlexNet4bottle
---|---|---|---
Dataset details | 151 back bottles | 151 back bottles | 24 black bottles
| 44 white bottles | 44 white bottles | 44 white bottles
|  | 35 silver bottles | 35 silver bottles
|  | 24 blacktumbler | 24 blacktumbler
TABLE I: Details of datasets for classic AlexNet classifications

##  V Results and Discussion

As what has been mentioned in the proposed methods, we investigated the image
similarity match using two methods: classic Neural Network - AlexNet and
feature distance-based image search-Vearch. The results of the first method
were used as a baseline result to demonstrate the performance of Neural
Networks due to the challenges resulting from the limited size of dataset and
image quality, which motivated us to develop more efficient solutions, such as
feature-distance-based image similarity match method to identify the objects
in the image.

#### Performance of AlexNet on Similar Object Classification

The train/test datasets were split at an 80:20 ratio. Detailed information
about the datasets can be found in table I of the experiment section. Because
the datasets are small, it was found that 10 epochs were enough to train the
model and test the performance of trained models. The experiments on the
number of epochs didn’t reveal much valuable information about object
identification, hence those results won’t be discussed here. The overall
classification accuracy and loss of our experiment on AlexNet are demonstrated
in figure V. These figures show that it’s easy to train the model because the
datasets are small. However, because the datasets are too small, although the
accuracies of the train and test are good, the models weren’t able to
correctly identify bottles with a little bit of variation in shape or color.
The black tumbler bottle shown in figure 1 was predicted as a black bottle,
the silver water cup shown in figure 2 was also identified as a black bottle,
no matter the datasets were biased or not. The results in figure V also show
that when more classes and images were used, the classification accuracy
decreased. Generally speaking, classic models, such as AlexNet, are not fit
for our project task, because of the challenges for us to collect more images
and the nature of this task to identify nearly-identical objects. Therefore,
we adopted feature-distance based image similarity search methodology for this
project.

[h](topskip=0pt, botskip=0pt, midskip=0pt)fig5.pngBottle Images Provided by
Zerobox Inc. The colors of the bottles are (from right to left, from top to
bottom): babyblue01, white02, beige01, red01, lavender01, yellow02, blue,
white03, white, red02, black tumbler, yellow03, white01, silver, black
bottles, babyblue02, black cup, white cup.

[h](topskip=0pt, botskip=0pt, midskip=0pt)fig6.png Loss and Accuracy curves of
AlexNet over ten epochs on different datasets: a) two classes dataset: black
and black tumbler bottles; b) four classes dataset: black, black tumbler
bottle, silver and white-water bottles; c) four classes unbiased dataset with
the same numbers of black and black tumbler bottles.

Figure 1: Black tumbler bottle Figure 2: Silver water cup

#### Performance of Feature Distance based Image Search Methods

Our feature-distance-based image search is completed by using pre-trained
VGG16, pre-trained AlexNet and our new model - AlphaAlexNet to extract image
features, storing the indexed features in the Vearch and using Vearch to
perform image similarity search.

The test performance of pre-trained VGG16, pre-trained AlexNet, and
AlphaAlexNet on 18 bottle identifications are evaluated by confusion matrixes.
The detailed results about confusion matrixes are shown in table II to IV. The
descriptions of the classes of bottles are listed under each table. The number
after each class name indicates the minor differences in the colors of
bottles. For example, white01, white02, and white03 mean that the colors of
three classes of bottles are generally white, however, there are minor
differences among those three white colors. From table II to IV, we can tell
that there 3 classes were mislabeled as other classes by the combination of
pre-trained VGG16 and Vearch among 25 test bottles:

  * •

one white01 bottle was mislabeled as a babyblue2 bottle,

  * •

one white02 was mislabeled as a babyblue01 bottle,

  * •

one white03 bottle was mislabeled as a beige01 bottle.

There were 4 classes that were mislabeled as other classes by the combination
of pre-trained AlexNet and Vearch among 25 test bottles:

  * •

one beige01 bottle was mislabeled as a white02 bottle,

  * •

one white01 bottle was mislabeled as a babyblue02 bottle,

  * •

one white02 bottle was mislabeled as a babyblue01 bottle,

  * •

one white03 bottle was mislabeled as a white02 bottle.

There were 3 classes that were mislabeled as other classes by the combination
of AlphaAlexNet and Vearch among 25 test bottles:

  * •

one black bottle was labeled as a silver bottle,

  * •

one white01 bottle was labeled as a babyblue02 bottle,

  * •

one white03 bottle was labeled as a white02 bottle.

From the mislabeled bottles, it can be observed that it’s very difficult for
neural networks to distinguish bottles that have close colors and the same
shapes. For example, the shape of white02 and white03 was almost the same,
their colors were very close; the shape of white01 and babyblue02 was very
close. Because the added six extra alpha channels put more weight on shapes
and colors, the combination of AlphaAlexNet and Vearch had better performance
to match similar images.

The overall identification accuracies of each neural networks are summarized
in Table V. When the image features extracted by the pre-trained VGG16 were
fed to Vearch, Vearch performed much better to match images than what it did
when the pre-trained AlexNet was used. Its accuracy value is 88% which is
higher than the 84% of pre-trained AlexNet, this might result from the fact
that VGG has more weight layers [59]. When Our new AlphaAlexNet was used to
extract image features, Vearch had an accuracy of 88% in image similarity
search. In the AlphaAlexNet, we added 6 extra alpha channels to the model. The
6 channels information comes from inverse colors and rotation of the image.
This helped to make the model distinguish close colors and less sensitive to
the small variations of shapes. The performance of our AlphaAlexNet with
Vearch was also illustrated here with search samples. Figure 3 shows the
correctly returned images when a query image was sent to Vearch. Figure 3
shows the wrong search results when a query image was sent to Vearch. The
wrong returned search results indicate that it’s a great challenge to search
the correct images if the shape or the colors of the water bottles were close.

The results shown in Table II and illustrated in figure 3 and 3 demonstrate
that the combination of Vearch and neural networks didn’t perform perfectly or
reach our expectations in terms of search accuracy. This should have resulted
from the fact that the subtle differences among the color and shape of bottles
are not well captured by neural networks. However, our AlphaAlexNet model,
which was developed based on existing AlexNet, could improve Vearch’s
similarity search performance for our task. This implies that the combination
of Vearch and neural networks have the potential to have much better
accuracies if more efficient neural networks could be developed to extract
features or bigger datasets could be obtained to train the neural networks.
The potential

No. | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 | C17 | C18
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
C1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C3 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0
C13 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C14 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C15 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
C17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
C18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
TABLE II: Confusion Matrix of Image Identification: Pre-trained VGG16 + Vearch. Classes C01 - C18: 1 Babyblue01, 2 Babyblue02, 3 Beige01, 4 Black bottle, 5 Black cup, 6 Black tumbler, 7 blue, 8 lavender01, 9 red01, 10 red02, 11 silver, 12 white, 13 white01, 14 white02, 15 white03, 16 white cup, 17 yellow02, 18 yellow03 No. | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 | C17 | C18
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
C1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
C4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0
C13 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C14 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
C16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
C17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
C18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
TABLE III: Confusion Matrix of Image Identification: Pre-trained AlexNet + Vearch. Classes C01 - C18: 1 Babyblue01, 2 Babyblue02, 3 Beige01, 4 Black bottle, 5 Black cup, 6 Black tumbler, 7 blue, 8 lavender01, 9 red01, 10 red02, 11 silver, 12 white, 13 white01, 14 white02, 15 white03, 16 white cup, 17 yellow02, 18 yellow03 No. | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 | C17 | C18
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
C1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C3 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0
C13 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
C14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
C15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
C16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
C17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
C18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
TABLE IV: Confusion Matrix of Image Identification: AlphaAlexNet + Vearch. Classes C01 - C18: 1 Babyblue01, 2 Babyblue02, 3 Beige01, 4 Black bottle, 5 Black cup, 6 Black tumbler, 7 blue, 8 lavender01, 9 red01, 10 red02, 11 silver, 12 white, 13 white01, 14 white02, 15 white03, 16 white cup, 17 yellow02, 18 yellow03 Feature Extraction Model | Pre-trained VGG16 | Pre-trained AlexNet | AlphaAlexNet
---|---|---|---
Image Search Accuracy | 88% | 84% | 88%
TABLE V: Image Search Accuracy of Vearch with different feature extraction
methods Figure 3: Real Search Examples - Correct Returns

[h!](topskip=0pt, botskip=0pt, midskip=0pt)fig10.png Real Search Examples -
False Returns

of Veach’s commercialization in the product re-identification could also be
supported by the work of JD e-commerce [23]. The combination of Vearch and
neural networks has many advantages over neural network classifications, such
as AlexNet classifications. It can perform image search at a very quick speed,
and the speed doesn’t deteriorate significantly when dataset size and object
classes increase. However, as we have discussed earlier. More work should be
done to improve its accuracy.

##  VI Ongoing Research: Siamese-AlexNet for Final Discretion

We have been developing a method to improve Vearch’s product identification
algorithm. Currently, Vearch returns similarity search results by ranking the
search results based on the feature distance between images. In our case,
after Vearch returned an image of a water bottle, a decision should be made
whether this query image is in the same category as the returned image or
whether a new type of bottle is detected. To improve the accuracy of this
decision, we designed a new model “Siamese-Alexnet”. The model architecture is
shown in figure 4. In training, an accuracy of 100% was achieved. However,
this network produced an accuracy of 50% in real query cases. The result isn’t
expected. The main reason for this should be the small size of the training
dataset. We are working on improving this network. This will also be part of
our future work get enough images to train this model or improve model
architecture for this job.

Figure 4: SimameseAlexNet Architecture

##  VII Conclusion and Future Work

In this work, we investigated solutions to tackle a nearly identical object
re-identification problem, which means to identify an object and decide the
category it belongs to. A water bottle dataset consisting of 400 images of 18
different types of bottles is used. The classic classification method was
tested, which wasn’t suitable for this object re-identification problem. Then
feature distance-based methodology, the combination of image feature
extraction neural networks, such as AlexNet and VGG16 and image search engine
- Vearch, were used to perform re-identification of objects. Our new model -
AlphaAlexNet, an improved model based on AlexNet, could improve the image
search performance of Vearch. This indicates that the combination of improved
neural networks and Vearch could have great potential to tackle the object
identifications problems in manufacturing. Based on the challenges that we
were facing, our experimental results, and research on exiting neural
networks, we concluded that we can do the following to develop an efficient
method to re-identify objects with high accuracy in our future work:

  * •

The combination of neural networks and Vearch has great potential. However,
more efficient feature extraction neural networks should be developed with a
focus on the color and shape features of objects with simple geometric and
textural properties.

  * •

A new re-identification neural network should be introduced to Vearch to help
Vearch to make identification decisions. This network could be the Siamese-
AlexNet that we have designed or another network.

  * •

Throughout the work of this project, the biggest challenge has always been
lacking a high-quality image dataset of products, in our case, water bottles.
Moreover, in the production settings, it’s doubtful whether the increase of
dataset size could improve the performance of the classic neural networks
because of the nature of the production re-identifications problem - to
identify nearly identical problems, which means the products in the images
won’t have that much different. Therefore, a new algorithm that is more
suitable for production re-identifications with limited images should be
introduced, just like that DifferNet can solve the surface defects detection
problem with as little as 16 images.

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