MULTIPLE KERNEL AND MULTI-LABEL LEARNING FOR IMAGE CATEGORIZATION By Serhat Selcuk Bucak One crucial step in recovering useful information from large image collections is image categorization . The goal of image categorization is to find the relevant labels for a given image from a closed set of labels . We propose two approximate optimization algorithms : the first one based on traditional sampling methods , the second one based on a variational bound approximation of the true objective function . We apply this novel representation to the image categorization problem and compare its performance to traditional kernel-based methods . We demonstrate on the PASCAL VOC 2007 dataset a consistent increase in classification accuracy . Our method allows to deal with different scenarii , where isolated images or groups of images are used for training and testing . The results obtained on an image categorization problem comprising of 3849 images from 101 landmarks of Singapore show that ( a ) the procedure to automatically induce concepts from an image is effective , and ( b ) the use of spatial relationships , in addition to concepts , for representing an image content helps improve the classifier accuracy . This approach is the first one , to our knowledge , to present a complete extension of the language modeling approach from information retrieval to the problem of graph-based image categorization and retrieval . Image categorization research has created knowledge on the types of attributes humans use in interpreting image similarity . Little effort has gone into studying the effect of any wider contextual factors on the image groupings people create . Nowadays , with the development of high quality graphical softwares , almost every presentation , in addition to text , contains some kind of images too . According to the presentation needs , different kinds of images are used by the presenters but different kinds of images needs different type of treatments which evolve the image categorization research . In our work we try to categorize images into two broad groups as Geometrical and Non-geometrical . In this approach , we represent the image of a scene by a collection of local regions , denoted as codewords . The image codewords which include visual characters of images are training units prepared for image categorizing . After learning , we get the LDA model parameters for each category image , and then in categorizing , we classify the input image by selecting the best category model . We have performed these user interaction properties and photo properties analysis task on eight major cities ’ Instagram posts and further classified the posts of these eight cities in five categories using Non-negative matrix factorization and latent Dirichlet allocation algorithm . The four prime influencing analyses have been computed to get ecology of the users on Instagram photo posts , which are Time based analysis ( TBA ) , Image Filter analysis ( IFA ) , Image Hashtags analysis ( IHA ) and Image categorization analysis ( ICA ) . Henceforth , this multivariate feature based Instagram analysis will help users to gain insight of popular content and make their respective content popular so as to reach out to a maximum number of people . MethodWe observe that many images embedded in biomedical publications carry versatile annotation text . Based on the locations of and the spatial relationships between these text elements in an image , we thus propose some novel image features for image categorization purpose , which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image . We further adopt a sparse coding representation ( SCR ) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features . In the following , we present a synopsis of papers collected in his special issue , which address some aspects of the above reearch problems . More specifically , some papers study challenging ssues , such as sparse representation , dictionary learning , transfer earning and unsupervised learning , in machine learning and attern recognition , while others target at solving real-world probems , such as image classification/categorization , motion detection , edical diagnosis , web ranking , natural language modeling , huan pose estimation , image inpainting , video quality assessment nd so on . Deep neural networks paved the way for significant improvements in image visual categorization during the last years . However , even though the tasks are highly varying , differing in complexity and difficulty , existing solutions mostly build on the same architectural decisions . As a widely - used regularization method , orthogonal regularization has proven to be effective in eliminating the correlation between convolution filters , facilitating gradient propagation and improving the compactness of the learned model . In addition , recent studies have demonstrated the advantage of orthogonal regularization in enhancing the network generalization ability in applications of deep hashing and image classification . According to our experiments , we empirically find that the orthogonal initialization of the convolution filters works well in suppressing the above mentioned visual artifacts . Image classification and object detection are two fundamental computer vision tasks . Object detectors are often trained through a reduction that converts object detection into an image classification problem . This reduction introduces a new challenge that is not found in natural image classification tasks : the training set is distinguished by a large imbalance between the number of annotated objects and the number of background examples ( image regions not belonging to any object class of interest ) . [ reference ] showed the use of joint training of a CNN and an MRF for human pose estimation , while Chen et al . [ reference ] focused on the image classification problem with a similar approach . Another prominent work is [ reference ] , in which the authors express deformable part models , a kind of MRF , as a layer in a neural network . In these methods , new images are synthesized by minimizing the content loss with respect to one input sample and the style loss with respect to one or more input samples . The content loss is typically the encoding of the image by a network training for an image categorization task , similar to our work . The style loss compares the statistics of the activations in various layers of the neural network . Trained as an image classifier , the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data . The model , which is a simple and elegant end - to - end architecture , outperforms standard data augmentation and fine - tuning techniques for the task of image - level classification as well . section : Introduction In part-based categorization context , the ability to learn representative feature from quantitative tiny object parts is of similar importance as to exactly localize the parts . We propose a new deep net structure for fine-grained categorization that follows the taxonomy workflow , which makes it interpretable and understandable for humans . By training customized sub-nets on each manually annotated parts , we increased the state-of-the-art part-based classification accuracy for general fine-grained CUB-200 - 2011 dataset by 2.1 % . These coarse labels reveal the most characteristic features of the entity in the image , which help us to re-rank the sequence of candidate subcategories and acquire the foremost subcategory as the final prediction . Our CLRKR approach achieves the best performance according to the experimental results on the widely-used CUB-200 - 2011 dataset for fine-grained visual categorization comparing with the state-of-the-art methods . We do not use any object/part annotation ( weakly supervised ) in the training or in the testing stage , but only class labels for training images . Fine-grained image categorization aims to classify objects with only subtle distinctions ( e.g. , two breeds of dogs that look alike ) . Most existing works heavily rely on object/part detectors to build the correspondence between object parts , which require accurate object or object part annotations at least for training images . Fine-grained image categorization is still a challenging computer vision problem in recent years . Most of existing methods highly rely on massive labeled data which are scarce in many real world applications . Cheap and small cameras , smart-phones with impressive camera quality , wearable computer devices and social networks have led to an explosion of image and video content and created a critical need for computer vision algorithms that enable an automatic classification and description of persons in images and videos . Most state-of-the-art approaches in object detection , recognition , and fine-grained visual classification are based on detecting particular parts of objects and are extracting features around those parts . An accurate and robust localization of such parts is crucial for the final accuracy of the part-based models but is the computational bottleneck of many approaches . To alleviate this , we propose an automatic maize tassel flowering status ( i.e. , non-flowering , partially-flowering and fully-flowering ) recognition method via the computer vision technology in this paper . In particular , this task is formulated as a fine-grained image categorization problem . More specifically , scale-invariant feature transform ( SIFT ) is first extracted as the low-level visual descriptor to characterize the maize flower . In this talk , I will present our work in developing automatic habitat classification solutions based on ground photographs . We employ image analysis methodologies from the multimedia and computer vision literature and formulate habitat classification as a fine-grained visual categorization problem . We treat digital photograph based habitat classification as an image-labeling problem and have developed an image-annotation framework that uses a random forest to automatically label a digital photo with habitat classes . Large-Scale Fine-Grained Visual Categorization of Birds . We address the problem of large-scale fine-grained visual categorization , describing new methods we have used to produce an online field guide to 500 North American bird species . We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds . Furthermore , we demonstrate that the robustness of the framework and the convergence rate can be further enhanced by appropriate regularizers over the adversarial distribution . The empirical study on real-world fine-grained visual categorization and digits recognition tasks verifies the effectiveness and efficiency of the proposed framework . Deep convolutional neural networks ( ConvNets ) have made significant progress in the past years , achieving recognition accuracy surpassing human beings in large - scale object recognition . The ConvNet models pre - trained on ImageNet have been proven to benefit a multitude of other computer vision tasks , ranging from fine - grained visual categorization ( FGVC ) , object detection , semantic segmentation to scene parsing , where labeled data are insufficient for training from scratch . The common layers such as convolution , non - linear rectification , pooling and batch normalization have become off - the - shelf commodities , widely supported on devices including workstations , PCs and embedded systems . section : Experiments We evaluate the proposed method on both large - scale image classification and challenging fine - grained visual categorization tasks . We make experiments using two PCs each of which is equipped with a 4 - core Intel i7 - 4790k@4.0GHz CPU , 32 G RAM , 512 GB Samsung PRO SSD and two Titan Xp GPUs . As in , we report the results on the validation set . For fine - grained categorization , we use three popular fine - grained benchmarks , i.e. , CUB - 200 - 2011 ( Birds ) , FGVC - aircraft ( Aircrafts ) and Stanford cars ( Cars ) . The Birds dataset contains 11 , 788 images from 200 species , with large intra - class variation but small inter - class variation . The error rate is 23.73 if the dimension is compressed to 2 K , still outperforming the original ResNet - 50 which performs global average pooling . subsection : Fine - grained Visual Categorization ( FGVC ) Finally , we apply iSQRT - COV models pre - trained on ImageNet to FGVC . Sentences in this corpus are parsed and all phrases along with the sentences are fully annotated with 5 labels : very positive , positive , neural , negative , very negative . We consider two classification tasks on this dataset : fine - grained classification with 5 labels and binary classification by removing neural labels . For the binary classification , the dataset has a split of train ( 6920 ) There is a trade - off between execution time and classification error . A larger value of leads to higher accuracy at the cost of executing more components for fine categorization . By enabling conditional executions with hyperparameter , we obtain a substantial X speed up with merely a minor increase in error from to ( Table [ reference ] ) . subsection : Model Overview Motivated by the limitation of encoding - based models for text classification , which is lacking the fine - grained classification clue , we propose a novel framework , named EXplicit interAction Model ( EXAM ) , leveraging the interaction mechanism to incorporate word - level matching signals . As can be seen from Figure [ reference ] , EXAM mainly contains three components : A word - level encoder to project the input text into a word - level representation . section : Introduction The problem of visual fine - grained categorization can be extremely challenging due to the subtle differences in the appearance of certain parts across related categories . In contrast to basic - level recognition , fine - grained categorization aims to distinguish between different breeds or species or product models , and often requires distinctions that must be conditioned on the object pose for reliable identification . subsection : Fine - grained categorization Recently , a large body of computer vision research has focused on the fine - grained classification problem in a number of domains , such as animal breeds or species , plant species , and man - made objects . Several approaches are based on detecting and extracting features from certain parts of objects . document : Evaluation of Output Embeddings for Fine - Grained Image Classification Image classification has advanced significantly in recent years with the availability of large - scale image sets . Specifically , we aim to confuse the network , by minimizing the distance between the predicted probability distributions for random pairs of samples from the training set . To do so , we propose Pairwise Confusion ( PC ) [ reference ] , a pairwise algorithm for training convolutional neural networks ( CNNs ) end - to - end for fine - grained visual classification . In Pairwise Confusion , we construct a Siamese neural network trained with a novel loss function that attempts to bring class conditional probability distributions closer to each other .
MULTIPLE KERNEL AND MULTI-LABEL LEARNING FOR IMAGE CATEGORIZATION By Serhat Selcuk Bucak One crucial step in recovering useful information from large image collections is image categorization . The goal of image categorization is to find the relevant labels for a given image from a closed set of labels .
We propose two approximate optimization algorithms : the first one based on traditional sampling methods , the second one based on a variational bound approximation of the true objective function . We apply this novel representation to the image categorization problem and compare its performance to traditional kernel-based methods . We demonstrate on the PASCAL VOC 2007 dataset a consistent increase in classification accuracy .
Our method allows to deal with different scenarii , where isolated images or groups of images are used for training and testing . The results obtained on an image categorization problem comprising of 3849 images from 101 landmarks of Singapore show that ( a ) the procedure to automatically induce concepts from an image is effective , and ( b ) the use of spatial relationships , in addition to concepts , for representing an image content helps improve the classifier accuracy . This approach is the first one , to our knowledge , to present a complete extension of the language modeling approach from information retrieval to the problem of graph-based image categorization and retrieval .
Image categorization research has created knowledge on the types of attributes humans use in interpreting image similarity . Little effort has gone into studying the effect of any wider contextual factors on the image groupings people create .
Nowadays , with the development of high quality graphical softwares , almost every presentation , in addition to text , contains some kind of images too . According to the presentation needs , different kinds of images are used by the presenters but different kinds of images needs different type of treatments which evolve the image categorization research . In our work we try to categorize images into two broad groups as Geometrical and Non-geometrical .
In this approach , we represent the image of a scene by a collection of local regions , denoted as codewords . The image codewords which include visual characters of images are training units prepared for image categorizing . After learning , we get the LDA model parameters for each category image , and then in categorizing , we classify the input image by selecting the best category model .
We have performed these user interaction properties and photo properties analysis task on eight major cities ’ Instagram posts and further classified the posts of these eight cities in five categories using Non-negative matrix factorization and latent Dirichlet allocation algorithm . The four prime influencing analyses have been computed to get ecology of the users on Instagram photo posts , which are Time based analysis ( TBA ) , Image Filter analysis ( IFA ) , Image Hashtags analysis ( IHA ) and Image categorization analysis ( ICA ) . Henceforth , this multivariate feature based Instagram analysis will help users to gain insight of popular content and make their respective content popular so as to reach out to a maximum number of people .
MethodWe observe that many images embedded in biomedical publications carry versatile annotation text . Based on the locations of and the spatial relationships between these text elements in an image , we thus propose some novel image features for image categorization purpose , which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image . We further adopt a sparse coding representation ( SCR ) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features .
In the following , we present a synopsis of papers collected in his special issue , which address some aspects of the above reearch problems . More specifically , some papers study challenging ssues , such as sparse representation , dictionary learning , transfer earning and unsupervised learning , in machine learning and attern recognition , while others target at solving real-world probems , such as image classification/categorization , motion detection , edical diagnosis , web ranking , natural language modeling , huan pose estimation , image inpainting , video quality assessment nd so on .
Deep neural networks paved the way for significant improvements in image visual categorization during the last years . However , even though the tasks are highly varying , differing in complexity and difficulty , existing solutions mostly build on the same architectural decisions .
As a widely - used regularization method , orthogonal regularization has proven to be effective in eliminating the correlation between convolution filters , facilitating gradient propagation and improving the compactness of the learned model . In addition , recent studies have demonstrated the advantage of orthogonal regularization in enhancing the network generalization ability in applications of deep hashing and image classification . According to our experiments , we empirically find that the orthogonal initialization of the convolution filters works well in suppressing the above mentioned visual artifacts .
Image classification and object detection are two fundamental computer vision tasks . Object detectors are often trained through a reduction that converts object detection into an image classification problem . This reduction introduces a new challenge that is not found in natural image classification tasks : the training set is distinguished by a large imbalance between the number of annotated objects and the number of background examples ( image regions not belonging to any object class of interest ) .
[ reference ] showed the use of joint training of a CNN and an MRF for human pose estimation , while Chen et al . [ reference ] focused on the image classification problem with a similar approach . Another prominent work is [ reference ] , in which the authors express deformable part models , a kind of MRF , as a layer in a neural network .
In these methods , new images are synthesized by minimizing the content loss with respect to one input sample and the style loss with respect to one or more input samples . The content loss is typically the encoding of the image by a network training for an image categorization task , similar to our work . The style loss compares the statistics of the activations in various layers of the neural network .
Trained as an image classifier , the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data . The model , which is a simple and elegant end - to - end architecture , outperforms standard data augmentation and fine - tuning techniques for the task of image - level classification as well . section : Introduction
In part-based categorization context , the ability to learn representative feature from quantitative tiny object parts is of similar importance as to exactly localize the parts . We propose a new deep net structure for fine-grained categorization that follows the taxonomy workflow , which makes it interpretable and understandable for humans . By training customized sub-nets on each manually annotated parts , we increased the state-of-the-art part-based classification accuracy for general fine-grained CUB-200 - 2011 dataset by 2.1 % .
These coarse labels reveal the most characteristic features of the entity in the image , which help us to re-rank the sequence of candidate subcategories and acquire the foremost subcategory as the final prediction . Our CLRKR approach achieves the best performance according to the experimental results on the widely-used CUB-200 - 2011 dataset for fine-grained visual categorization comparing with the state-of-the-art methods .
We do not use any object/part annotation ( weakly supervised ) in the training or in the testing stage , but only class labels for training images . Fine-grained image categorization aims to classify objects with only subtle distinctions ( e.g. , two breeds of dogs that look alike ) . Most existing works heavily rely on object/part detectors to build the correspondence between object parts , which require accurate object or object part annotations at least for training images .
Fine-grained image categorization is still a challenging computer vision problem in recent years . Most of existing methods highly rely on massive labeled data which are scarce in many real world applications .
Cheap and small cameras , smart-phones with impressive camera quality , wearable computer devices and social networks have led to an explosion of image and video content and created a critical need for computer vision algorithms that enable an automatic classification and description of persons in images and videos . Most state-of-the-art approaches in object detection , recognition , and fine-grained visual classification are based on detecting particular parts of objects and are extracting features around those parts . An accurate and robust localization of such parts is crucial for the final accuracy of the part-based models but is the computational bottleneck of many approaches .
To alleviate this , we propose an automatic maize tassel flowering status ( i.e. , non-flowering , partially-flowering and fully-flowering ) recognition method via the computer vision technology in this paper . In particular , this task is formulated as a fine-grained image categorization problem . More specifically , scale-invariant feature transform ( SIFT ) is first extracted as the low-level visual descriptor to characterize the maize flower .
In this talk , I will present our work in developing automatic habitat classification solutions based on ground photographs . We employ image analysis methodologies from the multimedia and computer vision literature and formulate habitat classification as a fine-grained visual categorization problem . We treat digital photograph based habitat classification as an image-labeling problem and have developed an image-annotation framework that uses a random forest to automatically label a digital photo with habitat classes .
Large-Scale Fine-Grained Visual Categorization of Birds . We address the problem of large-scale fine-grained visual categorization , describing new methods we have used to produce an online field guide to 500 North American bird species . We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds .
Furthermore , we demonstrate that the robustness of the framework and the convergence rate can be further enhanced by appropriate regularizers over the adversarial distribution . The empirical study on real-world fine-grained visual categorization and digits recognition tasks verifies the effectiveness and efficiency of the proposed framework .
Deep convolutional neural networks ( ConvNets ) have made significant progress in the past years , achieving recognition accuracy surpassing human beings in large - scale object recognition . The ConvNet models pre - trained on ImageNet have been proven to benefit a multitude of other computer vision tasks , ranging from fine - grained visual categorization ( FGVC ) , object detection , semantic segmentation to scene parsing , where labeled data are insufficient for training from scratch . The common layers such as convolution , non - linear rectification , pooling and batch normalization have become off - the - shelf commodities , widely supported on devices including workstations , PCs and embedded systems .
section : Experiments We evaluate the proposed method on both large - scale image classification and challenging fine - grained visual categorization tasks . We make experiments using two PCs each of which is equipped with a 4 - core Intel i7 - 4790k@4.0GHz CPU , 32 G RAM , 512 GB Samsung PRO SSD and two Titan Xp GPUs .
As in , we report the results on the validation set . For fine - grained categorization , we use three popular fine - grained benchmarks , i.e. , CUB - 200 - 2011 ( Birds ) , FGVC - aircraft ( Aircrafts ) and Stanford cars ( Cars ) . The Birds dataset contains 11 , 788 images from 200 species , with large intra - class variation but small inter - class variation .
The error rate is 23.73 if the dimension is compressed to 2 K , still outperforming the original ResNet - 50 which performs global average pooling . subsection : Fine - grained Visual Categorization ( FGVC ) Finally , we apply iSQRT - COV models pre - trained on ImageNet to FGVC .
Sentences in this corpus are parsed and all phrases along with the sentences are fully annotated with 5 labels : very positive , positive , neural , negative , very negative . We consider two classification tasks on this dataset : fine - grained classification with 5 labels and binary classification by removing neural labels . For the binary classification , the dataset has a split of train ( 6920 )
There is a trade - off between execution time and classification error . A larger value of leads to higher accuracy at the cost of executing more components for fine categorization . By enabling conditional executions with hyperparameter , we obtain a substantial X speed up with merely a minor increase in error from to ( Table [ reference ] ) .
subsection : Model Overview Motivated by the limitation of encoding - based models for text classification , which is lacking the fine - grained classification clue , we propose a novel framework , named EXplicit interAction Model ( EXAM ) , leveraging the interaction mechanism to incorporate word - level matching signals . As can be seen from Figure [ reference ] , EXAM mainly contains three components : A word - level encoder to project the input text into a word - level representation .
section : Introduction The problem of visual fine - grained categorization can be extremely challenging due to the subtle differences in the appearance of certain parts across related categories . In contrast to basic - level recognition , fine - grained categorization aims to distinguish between different breeds or species or product models , and often requires distinctions that must be conditioned on the object pose for reliable identification .
subsection : Fine - grained categorization Recently , a large body of computer vision research has focused on the fine - grained classification problem in a number of domains , such as animal breeds or species , plant species , and man - made objects . Several approaches are based on detecting and extracting features from certain parts of objects .
document : Evaluation of Output Embeddings for Fine - Grained Image Classification Image classification has advanced significantly in recent years with the availability of large - scale image sets .
Specifically , we aim to confuse the network , by minimizing the distance between the predicted probability distributions for random pairs of samples from the training set . To do so , we propose Pairwise Confusion ( PC ) [ reference ] , a pairwise algorithm for training convolutional neural networks ( CNNs ) end - to - end for fine - grained visual classification . In Pairwise Confusion , we construct a Siamese neural network trained with a novel loss function that attempts to bring class conditional probability distributions closer to each other .
MULTIPLE KERNEL AND MULTI-LABEL LEARNING FOR IMAGE CATEGORIZATION Modeling images as mixtures of reference images Integration of spatial relationships in visual language model for scene retrieval The effect of page context on magazine image categorization Chapter 17: Geometrical versus Non-geometrical Image Categorization Using Horizontal and Vertical Color Features An approach of categorize natural scene images based on visual characters and LDA model Multivariate Features Based Instagram Post Analysis to Enrich User Experience Categorizing biomedicine images using novel image features and sparse coding representation Special issue on selected and extended papers from the 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015) L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising Training Region-Based Object Detectors with Online Hard Example Mining Conditional Random Fields as Recurrent Neural Networks Unsupervised Cross-Domain Image Generation Weakly Supervised Deep Detection Networks Fine-Grained Categorization by Deep Part-Collaboration Convolution Net Coarse Label Refined Knowledge Reasoning for Fine-Grained Visual Categorization Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation Semi-Supervised Fine-Grained Image Categorization Using Transfer Learning With Hierarchical Multi-Scale Adversarial Networks Real Time Detection of Human Body and Face Parts Towards fine-grained maize tassel flowering status recognition: Dataset, theory and practice Grass, Scrub, Tree, Random Forest and Human in the Loop: Automatic habitat classification based on digital photographs Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds Robust Optimization over Multiple Domains Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization A C-LSTM Neural Network for Text Classification HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition Explicit Interaction Model towards Text Classification Part-Based R-CNNs for Fine-Grained Category Detection Evaluation of output embeddings for fine-grained image classification Pairwise Confusion for Fine-Grained Visual Classification
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CVPR CBMI ASIST ITQM IEEE Transactions on Image Processing 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE International Conference on Computer Vision (ICCV) ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) DICTA IScIDE EMR@ICMR CVPR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv 2015 IEEE International Conference on Computer Vision (ICCV) AAAI ECCV 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV
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