The Bag-of-Words ( BoW ) model is a promising image representation for annotation . One critical limitation of existing BoW models is the semantic loss during the codebook generation process , in which BoW simply clusters visual words in Euclidian space . This paper presented a novel hybrid rendering system for forest scene that solved the classic problem of degraded quality of image-based representations at close distances by building an impostor rendering system on top of a full , geometry-based , forest rendering system . This enabled almost imperceptible switching between the two representations . As the pretrained CNN accepts only RGB images as the input , we apply continuous wavelet transform ( CWT ) to the ECG signals under analysis to generate an over-complete time – frequency representation . Then , we feed the resulting image-like representations as inputs into the pretrained CNN to generate the CNN features . Next , we train the additional fully connected network on the ECG labeled data represented by the CNN features in a supervised way by minimizing cross-entropy error with dropout regularization . These loci are very insensitive to image disturbances , in strong contrast to previously available medial loci , as demonstrated in a companion paper . Core-related geometric properties and image object representations are laid out which , together with the aforementioned insensitivities , allow the core to be used effectively for a variety of image analysis objectives . This is the first participation of the group at an accepted lab of the Cross Language Evaluation Forum . For image visual representation , various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors and the new Border Profile presented in this work , were adopted . Textual representation was obtained by vector quantisation on Bag-of-Words codebook generated using attribute importance derived from χ-test and the Characteristic Delimiters feature presented in this paper . Still , the degree of similarity is different in the different cases , with classification requiring more invariance to appearance variations and copy detection sensitivity to small image details . In order to learn an image representation that satisfies the different trade - offs , we start from an existing image classification network . We use a generalized mean layer that converts a spatial activation map to a fixed - size vector . To our knowledge , our method is the first one that trains a single network with input images of multiple sizes . subsubsection : Full - image Representations Improve Accuracy Next we investigate the accuracy of the full - image views . Although CNNs are very successful in image classification tasks , its use in text classification is relatively new and has some peculiarities . Contrasting with traditional image bi - dimensional representations , texts are one - dimensionally represented . Due to this property , the convolutions are designed as temporal convolutions . Object parsing , the segmentation of an object into semantic parts , is naturally performed by humans to obtain a more detailed understanding of the scene . When performed automatically by computers , it has many practical applications , such as in human - robot interaction , human behaviour analysis and image descriptions for the visually impaired . Furthermore , detailed part information has been shown to be beneficial in other visual recognition tasks such as fine - grained recognition [ reference ] , human pose estimation [ reference ] and object detection layer typically discards some pixel - wise information . For image restoration problems such as super - resolution and denoising , image details are very important . Therefore , most deep - learning approaches for such problems do not use pooling . Each layer can be interpreted as a non - linear transformation of the previous one with additional spatial invariance . We call these layers image feature maps , and formally define them as follows : theorem : . To cope with the lack of object - level annotations , several methods extract feature activations from convolutional neural networks ( CNN ) by scanning over different image regions . They then aggregate the extracted features into image - level representations for classification purpose . Under this scheme , regions that belong to the background are considered as important as regions that contain objects . Effective approaches have been designed for matching rigid objects across images in the presence of large viewpoint changes Lowe2004 , Barnes2010 , HaCohen2011 . However , the performance of current state - of - the - art matching algorithms for images ‘ subsection : Image Model The image model uses a CNN to get the representation of images . Specifically , the VGGNet is used to extract the image feature map from a raw image : Unlike previous studies that use features from the last inner product layer , we choose the features from the last pooling layer , which retains spatial information of the original images . Supervised metric learning plays a substantial role in statistical classification . Conventional metric learning algorithms have limited utility when the training data and testing data are drawn from related but different domains ( i.e. , source domain and target domain ) . However , most of the reported person Re-ID methods deal with the human regions of interest ( ROIs ) which are extracted manually with well-aligned bounding boxes . They mainly focus on designing discriminative feature descriptors and relevant metric learning on these manually-cropped human ROIs . This paper aims at answering two questions : ( 1 ) Do human detection and segmentation affect the performance of person re-identification ? ; ( 2 ) How to overcome the effect of human detection and segmentation with the state of the art method for person re-identification ? First , we show how to extend metric learning methods for domain adaptation , allowing for learning metrics independent of the domain shift and the final classifier used . Furthermore , we go beyond classical metric learning by extending the method to asymmetric , category independent transformations . Our framework can adapt features even when the target domain does not have any labeled examples for some categories , and when the target and source features have different dimensions . Many recent works have combined two machine learning topics , learning of supervised distance metrics and manifold embedding methods , into supervised nonlinear dimensionality reduction methods . We show that a combination of an early metric learning method and a recent unsupervised dimensionality reduction method empirically outperforms previous methods . In our method , the Riemannian distance metric measures local change of class distributions , and the dimensionality reduction method makes a rigorous tradeoff between precision and recall in retrieving similar data points based on the reduced-dimensional display . This paper presents an exemplar based metric learning framework dedicated to robust visual localization in complex scenes , e.g. street images . The proposed framework learns off-line a specific ( local ) metric for each image of the database , so that the distance between a database image and a query image representing the same scene is smaller than the distance between the current image and other images of the database . In this paper , we design a novel local metric learning approach to well handle exemplar-based object detection task . The main works are two-fold : 1 ) a novel local metric learning algorithm called exemplar metric learning ( EML ) is designed and 2 ) an exemplar-based object detection algorithm based on EML is implemented . We evaluate our method on two generic object detection data sets : UIUC-Car and UMass FDDB . In this paper , motivated by the co-training framework , we propose an algorithm-independent framework , named co-metric , to learn Mahalanobis metrics in multi-view settings . In its implementation , an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples . Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop . A key observation is that the saliency of a superpixel can be estimated by the distance from the most certain foreground and background seeds . Instead of measuring distance on the Euclidean space , we present a learning method based on two complementary Mahalanobis distance metrics : 1 ) generic metric learning ( GML ) and 2 ) specific metric learning ( SML ) . GML aims at the global distribution of the whole training set , while SML considers the specific structure of a single image . We demonstrate the robustness of the scheme on a very large scale , real-world dataset . Specifically , we show that the gender difference among related-pairs leads to lower performance of traditional verification and metric learning algorithms . Through various experiments , we quantitatively study the robustness of the proposed scheme in the general and specific case of different gender related-pairs , achieving up to 80 % , 75 % accuracy for the parent-child and siblings relations respectively . The extreme viewpoint variation ( up to 180 degrees ) poses great challenges for existing approaches . Inspired by the behavior in human 's recognition process , we propose a novel viewpoint-aware metric learning approach . It learns two metrics for similar viewpoints and different viewpoints in two feature spaces , respectively , giving rise to viewpoint-aware network ( VANet ) . However , both methods are not optimal because the dimension reduction does not consider the distance metric learning . In this paper , we extend the Bayesian face and KISSME approaches to cross - view metric learning , where we consider to learn a subspace with cross - view data , and at the same time learn a distance function in the dimensional subspace for the cross - view similarity measure . Suppose we have a cross - view training set of classes , where contains samples in a - dimensional space from one view , contains samples in the same - dimensional space but from the other view . Content-based image retrieval ( CBIR ) is one of the most active research areas . Many visual feature representations have been explored and many systems built . However , in most of current systems , only the global features such as overall color histogram and texture moments are used which ignore the actual composition of the image in terms of internal objects . Feature matching is a fundamental step in many real-time computer vision applications such as simultaneous localization and mapping , motion analysis , and stereo correspondence . The performance of these applications depends on the distinctiveness of the visual feature descriptors used , and the speed at which they can be extracted from video frames . When combined with standard key-point detectors , the rotation-aware binary robust independent elementary features ( rBRIEF ) descriptor has been shown to outperform its counterparts . In this chapter we expose the challenges that these techniques face . We show that if a lexicon of visual concepts is identified a priori , a statistical framework can be used to build visual feature models for the concepts in the lexicon . Using support vector machine ( SVM ) classification we build models for 34 semantic concepts for the TREC 2002 benchmark corpus . Object recognition and clustering are useful techniques in pattern recognition and computer vision . Traditionally , these techniques have been implemented by visual-feature-based methods . However , these methods may not adequately tackle the differences in the shapes and colors of objects . Semantically important features for summary generation are carefully analyzed , and two types of summaries , digest and highlight , are extracted . A digest extraction is based on an audio level estimation and visual features analysis , where digest shots are adaptively determined for user-specified duration by utilizing the MPEG-7 based motion features . A highlight-based summarization from TV broadcast sports programmes is achieved by analyzing audio class and audio level . For effective saliency detection , the original image is first over-segmented into a set of primitive superpixels . A visual feature set is designed to extract a regional feature histogram , which is then quantized based on the optimal parameters learned from the labeled SAR images . Afterward , three saliency measurements based on the specificity of the rivers in the SAR images are proposed to generate a single layer saliency map , i.e. , local region contrast , boundary connectivity , and edge density . Finally , we extracted total 6392 features from each input audio segment . subsubsection : Visual Feature Extraction To extract visual features , we focus not only on feature extraction from each video frame but also try to model temporal features across frames . We also explore how the information content of questions and their answers differs from image captions . For baselines , we offer several approaches that use a combination of both text and state - of - the - art visual features [ reference ] . As part of the VQA initiative , we will organize an annual challenge and associated workshop to discuss state - of - the - art methods and best practices . Our method outperforms the other existing method by about 7 % . Visualization of features in the last pooling layer is shown in Fig . 3 ( a )( b ) . Following this finding – and building on other recent work for finding simple network structures – we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets ( CIFAR - 10 , CIFAR - 100 , ImageNet ) . To analyze the network we introduce a new variant of the “ deconvolution approach ” for visualizing features learned by CNNs , which can be applied to a broader range of network structures than existing approaches . ⌈⌉ ⌊⌋ As these methods use auxiliary objectives divorced from the semantic clustering objective , it is unsurprising that they perform worse than IIC ( s : experiments ) , which directly optimises for it , training the network end - to - end with the final clusterer implicitly wrapped inside . paragraph : Optimising image - to - image distance . Many approaches to deep clustering , whether semantic or auxiliary , utilise a distance function between input images that approximates a given grouping criterion .
The Bag-of-Words ( BoW ) model is a promising image representation for annotation . One critical limitation of existing BoW models is the semantic loss during the codebook generation process , in which BoW simply clusters visual words in Euclidian space .
This paper presented a novel hybrid rendering system for forest scene that solved the classic problem of degraded quality of image-based representations at close distances by building an impostor rendering system on top of a full , geometry-based , forest rendering system . This enabled almost imperceptible switching between the two representations .
As the pretrained CNN accepts only RGB images as the input , we apply continuous wavelet transform ( CWT ) to the ECG signals under analysis to generate an over-complete time – frequency representation . Then , we feed the resulting image-like representations as inputs into the pretrained CNN to generate the CNN features . Next , we train the additional fully connected network on the ECG labeled data represented by the CNN features in a supervised way by minimizing cross-entropy error with dropout regularization .
These loci are very insensitive to image disturbances , in strong contrast to previously available medial loci , as demonstrated in a companion paper . Core-related geometric properties and image object representations are laid out which , together with the aforementioned insensitivities , allow the core to be used effectively for a variety of image analysis objectives .
This is the first participation of the group at an accepted lab of the Cross Language Evaluation Forum . For image visual representation , various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors and the new Border Profile presented in this work , were adopted . Textual representation was obtained by vector quantisation on Bag-of-Words codebook generated using attribute importance derived from χ-test and the Characteristic Delimiters feature presented in this paper .
Still , the degree of similarity is different in the different cases , with classification requiring more invariance to appearance variations and copy detection sensitivity to small image details . In order to learn an image representation that satisfies the different trade - offs , we start from an existing image classification network . We use a generalized mean layer that converts a spatial activation map to a fixed - size vector .
To our knowledge , our method is the first one that trains a single network with input images of multiple sizes . subsubsection : Full - image Representations Improve Accuracy Next we investigate the accuracy of the full - image views .
Although CNNs are very successful in image classification tasks , its use in text classification is relatively new and has some peculiarities . Contrasting with traditional image bi - dimensional representations , texts are one - dimensionally represented . Due to this property , the convolutions are designed as temporal convolutions .
Object parsing , the segmentation of an object into semantic parts , is naturally performed by humans to obtain a more detailed understanding of the scene . When performed automatically by computers , it has many practical applications , such as in human - robot interaction , human behaviour analysis and image descriptions for the visually impaired . Furthermore , detailed part information has been shown to be beneficial in other visual recognition tasks such as fine - grained recognition [ reference ] , human pose estimation [ reference ] and object detection
layer typically discards some pixel - wise information . For image restoration problems such as super - resolution and denoising , image details are very important . Therefore , most deep - learning approaches for such problems do not use pooling .
Each layer can be interpreted as a non - linear transformation of the previous one with additional spatial invariance . We call these layers image feature maps , and formally define them as follows : theorem : .
To cope with the lack of object - level annotations , several methods extract feature activations from convolutional neural networks ( CNN ) by scanning over different image regions . They then aggregate the extracted features into image - level representations for classification purpose . Under this scheme , regions that belong to the background are considered as important as regions that contain objects .
Effective approaches have been designed for matching rigid objects across images in the presence of large viewpoint changes Lowe2004 , Barnes2010 , HaCohen2011 . However , the performance of current state - of - the - art matching algorithms for images ‘
subsection : Image Model The image model uses a CNN to get the representation of images . Specifically , the VGGNet is used to extract the image feature map from a raw image : Unlike previous studies that use features from the last inner product layer , we choose the features from the last pooling layer , which retains spatial information of the original images .
Supervised metric learning plays a substantial role in statistical classification . Conventional metric learning algorithms have limited utility when the training data and testing data are drawn from related but different domains ( i.e. , source domain and target domain ) .
However , most of the reported person Re-ID methods deal with the human regions of interest ( ROIs ) which are extracted manually with well-aligned bounding boxes . They mainly focus on designing discriminative feature descriptors and relevant metric learning on these manually-cropped human ROIs . This paper aims at answering two questions : ( 1 ) Do human detection and segmentation affect the performance of person re-identification ? ; ( 2 ) How to overcome the effect of human detection and segmentation with the state of the art method for person re-identification ?
First , we show how to extend metric learning methods for domain adaptation , allowing for learning metrics independent of the domain shift and the final classifier used . Furthermore , we go beyond classical metric learning by extending the method to asymmetric , category independent transformations . Our framework can adapt features even when the target domain does not have any labeled examples for some categories , and when the target and source features have different dimensions .
Many recent works have combined two machine learning topics , learning of supervised distance metrics and manifold embedding methods , into supervised nonlinear dimensionality reduction methods . We show that a combination of an early metric learning method and a recent unsupervised dimensionality reduction method empirically outperforms previous methods . In our method , the Riemannian distance metric measures local change of class distributions , and the dimensionality reduction method makes a rigorous tradeoff between precision and recall in retrieving similar data points based on the reduced-dimensional display .
This paper presents an exemplar based metric learning framework dedicated to robust visual localization in complex scenes , e.g. street images . The proposed framework learns off-line a specific ( local ) metric for each image of the database , so that the distance between a database image and a query image representing the same scene is smaller than the distance between the current image and other images of the database .
In this paper , we design a novel local metric learning approach to well handle exemplar-based object detection task . The main works are two-fold : 1 ) a novel local metric learning algorithm called exemplar metric learning ( EML ) is designed and 2 ) an exemplar-based object detection algorithm based on EML is implemented . We evaluate our method on two generic object detection data sets : UIUC-Car and UMass FDDB .
In this paper , motivated by the co-training framework , we propose an algorithm-independent framework , named co-metric , to learn Mahalanobis metrics in multi-view settings . In its implementation , an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples . Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop .
A key observation is that the saliency of a superpixel can be estimated by the distance from the most certain foreground and background seeds . Instead of measuring distance on the Euclidean space , we present a learning method based on two complementary Mahalanobis distance metrics : 1 ) generic metric learning ( GML ) and 2 ) specific metric learning ( SML ) . GML aims at the global distribution of the whole training set , while SML considers the specific structure of a single image .
We demonstrate the robustness of the scheme on a very large scale , real-world dataset . Specifically , we show that the gender difference among related-pairs leads to lower performance of traditional verification and metric learning algorithms . Through various experiments , we quantitatively study the robustness of the proposed scheme in the general and specific case of different gender related-pairs , achieving up to 80 % , 75 % accuracy for the parent-child and siblings relations respectively .
The extreme viewpoint variation ( up to 180 degrees ) poses great challenges for existing approaches . Inspired by the behavior in human 's recognition process , we propose a novel viewpoint-aware metric learning approach . It learns two metrics for similar viewpoints and different viewpoints in two feature spaces , respectively , giving rise to viewpoint-aware network ( VANet ) .
However , both methods are not optimal because the dimension reduction does not consider the distance metric learning . In this paper , we extend the Bayesian face and KISSME approaches to cross - view metric learning , where we consider to learn a subspace with cross - view data , and at the same time learn a distance function in the dimensional subspace for the cross - view similarity measure . Suppose we have a cross - view training set of classes , where contains samples in a - dimensional space from one view , contains samples in the same - dimensional space but from the other view .
Content-based image retrieval ( CBIR ) is one of the most active research areas . Many visual feature representations have been explored and many systems built . However , in most of current systems , only the global features such as overall color histogram and texture moments are used which ignore the actual composition of the image in terms of internal objects .
Feature matching is a fundamental step in many real-time computer vision applications such as simultaneous localization and mapping , motion analysis , and stereo correspondence . The performance of these applications depends on the distinctiveness of the visual feature descriptors used , and the speed at which they can be extracted from video frames . When combined with standard key-point detectors , the rotation-aware binary robust independent elementary features ( rBRIEF ) descriptor has been shown to outperform its counterparts .
In this chapter we expose the challenges that these techniques face . We show that if a lexicon of visual concepts is identified a priori , a statistical framework can be used to build visual feature models for the concepts in the lexicon . Using support vector machine ( SVM ) classification we build models for 34 semantic concepts for the TREC 2002 benchmark corpus .
Object recognition and clustering are useful techniques in pattern recognition and computer vision . Traditionally , these techniques have been implemented by visual-feature-based methods . However , these methods may not adequately tackle the differences in the shapes and colors of objects .
Semantically important features for summary generation are carefully analyzed , and two types of summaries , digest and highlight , are extracted . A digest extraction is based on an audio level estimation and visual features analysis , where digest shots are adaptively determined for user-specified duration by utilizing the MPEG-7 based motion features . A highlight-based summarization from TV broadcast sports programmes is achieved by analyzing audio class and audio level .
For effective saliency detection , the original image is first over-segmented into a set of primitive superpixels . A visual feature set is designed to extract a regional feature histogram , which is then quantized based on the optimal parameters learned from the labeled SAR images . Afterward , three saliency measurements based on the specificity of the rivers in the SAR images are proposed to generate a single layer saliency map , i.e. , local region contrast , boundary connectivity , and edge density .
Finally , we extracted total 6392 features from each input audio segment . subsubsection : Visual Feature Extraction To extract visual features , we focus not only on feature extraction from each video frame but also try to model temporal features across frames .
We also explore how the information content of questions and their answers differs from image captions . For baselines , we offer several approaches that use a combination of both text and state - of - the - art visual features [ reference ] . As part of the VQA initiative , we will organize an annual challenge and associated workshop to discuss state - of - the - art methods and best practices .
Our method outperforms the other existing method by about 7 % . Visualization of features in the last pooling layer is shown in Fig . 3 ( a )( b ) .
Following this finding – and building on other recent work for finding simple network structures – we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets ( CIFAR - 10 , CIFAR - 100 , ImageNet ) . To analyze the network we introduce a new variant of the “ deconvolution approach ” for visualizing features learned by CNNs , which can be applied to a broader range of network structures than existing approaches . ⌈⌉ ⌊⌋
As these methods use auxiliary objectives divorced from the semantic clustering objective , it is unsurprising that they perform worse than IIC ( s : experiments ) , which directly optimises for it , training the network end - to - end with the final clusterer implicitly wrapped inside . paragraph : Optimising image - to - image distance . Many approaches to deep clustering , whether semantic or auxiliary , utilise a distance function between input images that approximates a given grouping criterion .
Semantics-preserving bag-of-words models for efficient image annotation Real-time rendering of dense forest scene using hybrid LOD and IBR approach Convolutional Neural Networks for Electrocardiogram Classification Zoom-Invariant Vision of Figural Shape: The Mathematics of Cores FHDO Biomedical Computer Science Group at Medical Classification Task of ImageCLEF 2015 MultiGrain: a unified image embedding for classes and instances Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Squeezed Very Deep Convolutional Neural Networks for Text Classification Holistic, Instance-Level Human Parsing Deeply-Recursive Convolutional Network for Image Super-Resolution Convolutional Kernel Networks ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks DeepMatching: Hierarchical Deformable Dense Matching Stacked Attention Networks for Image Question Answering Cross-Domain Metric Learning Based on Information Theory A Quantitative Analysis of the Effect of Human Detection and Segmentation Quality in Person Re-identification Performance Asymmetric and Category Invariant Feature Transformations for Domain Adaptation Supervised nonlinear dimensionality reduction by Neighbor Retrieval Exemplar based metric learning for robust visual localization Local Metric Learning for Exemplar-Based Object Detection Co-metric: a metric learning algorithm for data with multiple views Adaptive Metric Learning for Saliency Detection Can faces verify blood-relations? Vehicle Re-Identification With Viewpoint-Aware Metric Learning Person re-identification by Local Maximal Occurrence representation and metric learning Combine user defined region-of-interest and spatial layout for image retrieval High-Throughput and Area-Optimized Architecture for rBRIEF Feature Extraction Moderate Vocabulary Visual Concept Detection for the TRECVID 2002 A Semantic-Similarity-Based Method for Object Description and Clustering Automated MPEG audio-video summarization and description Visual Saliency Modeling for River Detection in High-Resolution SAR Imagery Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling VQA: Visual Question Answering Asymmetric Tri-training for Unsupervised Domain Adaptation Striving for Simplicity: The All Convolutional Net Invariant Information Clustering for Unsupervised Image Classification and Segmentation
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LS-MMRM@ACM Multimedia CLEF (Working Notes) ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence ICANN BMVC 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) International Journal of Computer Vision 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI ICASSP ICIP BTAS ICCV 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICIP SMC ICIP (1) Knowl. Based Syst. 2015 IEEE International Conference on Computer Vision (ICCV) ICML ICLR 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
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