These methods highly depends on the preprocessing of brain images , including image rigid registration and segmentation . Motivated by the success of deep learning in image classification , this paper proposes a new classification framework based on combination of 2D convolutional neural networks ( CNN ) and recurrent neural networks ( RNNs ) , which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices . The 2D CNNs are built to capture the features of image slices while the gated recurrent unit ( GRU ) of RNN is cascaded to learn and integrate the inter-slice features for image classification . The idea of making use of higher orders , or types , of fuzzy logic is to capture and represent uncertainty that is more complex . This paper is focused on image processing systems , which includes image segmentation , image filtering , image classification and edge detection . Various applications are presented where general type-2 fuzzy sets , interval type-2 fuzzy sets , and interval-value fuzzy sets are used ; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing . Discriminant function is an algorithm for dividing line , the result is to provide an identification of the boundary equa tion , the equation is the discriminant function , so the discriminant function to describe the types of the dividing line between the specific form . This article is mainly based on the discriminant function model , the image classification problem is studied , through the example to illustrate the feasibility and effectiveness of the method of classification . The experimental results show that the dis criminant function model has a certain practical value , the image classification problem , the method of discriminant function good classification performance , accuracy and efficiency , finally , use the MATLAB graphical user interface(GUI ) design method , a good human-computer interaction system main interface . In contrast to single images , image sets can capture a broad range of variations in the appearance of a single face or object . Recognition from these multiple images ( i.e. , image set classification ) has gained significant attention in the area of computer vision . Unlike many existing approaches , which assume that only the images in the same set affect each other , this work develops a group collaborative representation ( GCR ) model which makes no such assumption , and which can effectively determine the hidden structure among image sets . Classification of a ship based on satellite imagery usually results differently depending on the type of polarization used in image generation . Also , the different ship ’s orientation in each image degrades the performance of image-based classification . Given these points , Sentinel-1 data also needs some methods to classify the type of ship . The positive category is where the surface damage is present within the image , otherwise the image is classified as negative . This is an image-based classification . The ANN accepts image inputs that have been classified as positive by the ANN . We compare the original random forest algorithm with their ensemble counterparts and discuss the results . Finally we deal the problem of image data classification with both supervised ( ensemble ) learning and unsupervised learning . We apply the algorithms developed in the thesis for this task . Its features include : extending a keyword through bootstrapping construction . Aside from using bootstrapping construction to expand keywords and to classify images , we have also added a discriminative feature metric to increase the precision and recall rates of image classifying to our standards . Finally , we apply this technique on an image archives management system , named " Campus Image System " . Image recording and binarization . Image analysis classification of the arrangement : Analysis of mixed structures ; teaching of an image analysis classifier . Applications . A major issue concerning the design and implementation of an acquisition system for digital images of skin lesions is the ability of capturing reproducible images . The reproducibility is considered essential for image analysis classification and for the comparison of sequential images during follow-up studies . This paper describes a complete image acquisition system used for the collection of reproducible images of patients having melanoma and compares them with images displaying dysplastic nevus for diagnostic purposes . Both techniques have their own pros and cons in finding the solutions of many applications . To create land cover thematic maps with higher accuracies then the image classification analysis is a big challenge in the remote sensing . However , the pixel based classification technique works only on spectral features and neglects the spatial features but in object based classification have ability to work on both spectral and spatial features . Accuracy assessment should be a fundamental component of an image classification analysis and is typically undertaken following either a non-site- or a site-specific methodology . The assessment of classification accuracy is , however , often difficult , with many challenges associated with the ground data typically required . A with classifying the sound segment of the video at the sound segment classification unit ( 103 ) , by classifying the image segmentation of the image in the image segment classification unit ( 104 ) , classification results in a plurality generator ( 105 ) , a plurality of generating at least one sound segment classification results and a plurality of image segment classification result , the classification results pair generation unit at ( 106 ) , generate a plurality of classification results pair of these classification results , as candidates of the optimum segment classification result for the image and classification by comparing the classification result output unit the classification results between scores for each classification result pair calculated result between score calculator ( 107 ) ( 108 ) , a good correspondence take such sound segment classification result and the image segment and it outputs the classification result . Thus , without prior adjusting the parameters , a plurality of sound segment and a plurality of image segments included in the video , it can be accurately classified by the object . Conventional image categorization techniques primarily rely on low-level visual cues . In this paper , we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification . Specific to each image category , a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning [ 1 ] . We address this issue by proposing an effective global prior representation . Global average pooling is a good baseline model as the global contextual prior , which is commonly used in image classification tasks . In , it was successfully applied to semantic segmentation . We used the FCN - 8s architecture of [ reference ] as the first part of our network , which provides unary potentials to the CRF . This network is based on the VGG - 16 network [ reference ] but has been restructured to perform pixel - wise prediction instead of image classification . The complete architecture of our network , including the FCN8s part can be found in the appendix . The initial convolutional layers of the network extract features from the image while the fully connected layers predict the output probabilities and coordinates . Our network architecture is inspired by the GoogLeNet model for image classification . Our network has 24 convolutional layers followed by 2 fully connected layers . In this paper , we propose a novel deep network for WSOD . Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning ( MIL ) , our strategy generates proposal clusters to learn refined instance classifiers by an iterative process . The proposals in the same cluster are spatially adjacent and associated with the same object . Semantic image segmentation is an essential component of modern autonomous driving systems , as an accurate understanding of the surrounding scene is crucial to navigation and action planning . Current state - of - the - art approaches in semantic image segmentation rely on pretrained networks that were initially developed for classifying images as a whole . While these networks exhibit outstanding recognition performance ( i.e. , what is visible ? ) , they lack localization accuracy ( i.e. , where precisely is something located ? ) . In VggNet - PCA + LSTM , PCA is applied to decrease the feature dimension from 4096 to 1000 . VggNet + ft + LSTM applies a VggNet that has been fine - tuned on the target dataset , based on the task of image - attributes classification . Our Approaches : On a traffic sign recognition benchmark it outperforms humans by a factor of two . We also improve the state - of - the - art on a plethora of common image classification benchmarks . 04 - 12 We believe that the presented work is a step towards dedicated architectures for dense prediction that are not constrained by image classification precursors . As new sources of data become available , future architectures may be trained densely end - to - end , removing the need for pre - training on image classification datasets . This may enable architectural simplification and unification . Convolutional neural networks ( CNNs ) are a special type of artificial neural networks that learn a hierarchy of increasingly complex features by successive convolution , pooling and non - linear activation operations . Originally designed for image recognition and classification , CNNs are now commonly used in semantic image segmentation . A naive approach follows a sliding - window strategy , where regions defined by the window are processed independently . Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis . Due to the nuclei tiny size , significant inter-/intra-class variances , as well as the inferior image quality , previous automated methods would easily suffer from limited accuracy and robustness . It is common for words to be POS-tagged , where each word is associated with a tag that identifies its word class ( and perhaps more ) . The set of classes used , the tagset , may be akin to the conventional parts of speech but usually reflects a rather more fine-grained classification . For example , the CLAWS-5 tagset used in the British National Corpus ( BNC ) has 57 simple word class tags , and some others have many more still . This thesis tackles fine-grained image recognition , the task of sub-category or species classification . It explores general methods to improve fine-grained image classification including the use of generative models and deep convolutional neural networks leading to novel models such as a Mixture of deep convolution neural networks . This work led to 9 peer reviewed publications and a Best Paper Award . In this work , we attempt to classify commodities in containers with HS(harmonized system ) codes , which is a challenging task due to the large number of categories in HS codes and its hierarchical structure based on a product 's composition and economic activity . To tackle this problem , in this paper we propose an ensemble model which incorporates fine-grained image categorization , data analysis on cargo manifests , and human-in-the-loop paradigm . By employing deep learning , we train a triplet network for fine-grained image categorization . : Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification . Data augmentation is usually adopted to increase the amount of training data , prevent overfitting and improve the performance of deep models . The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task . Compared with the CNN method in image classification , the model is proficient in fine-grained classification problem and has a better robustness due to its mechanism of multi-glance and visual attention . We evaluate the model on vehicle dataset , where its performance exceeds CNN baseline on image classification . It also outperforms DBSCAN and its recent variation DSets-DBSCAN . We have chosen species recognition ( bird and butterflies ) as a representative fine-grained image categorization problem , though the proposed algorithm is a general approach applicable to other similar tasks . We also introduce a new benchmark fine-grained image dataset , that of Indian endemic butterfly species ( Titli.vl ) , which is available through the corresponding author . There are many types of butterflies , and the research on classification of butterfly species is of great significance in practical work such as environmental protection and control of agricultural and forest pests . Butterfly classification is a fine-grained image classification problem that is more difficult than generic image classification . Common butterfly photos are mostly specimen photos ( indoor photos ) and ecological photos ( outdoor photos/natural images ) . Hence , we introduce a generic framework to overcome the issue , integrating the graph convolutional network ( GCN ) and deep structured feature embedding ( DSFE ) into an end-to-end workflow . Furthermore , instead of using a classic graph convolutional neural network , we propose a gated graph convolutional network , which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification . Taking the semantic segmentation of building footprints as a practical example , we compared different feature embedding architectures and graph neural networks . Our k - means style objective is easy to optimize and flexible , supporting various forms of clustering , such as sample clustering , spatial clustering , as well as co - clustering . We demonstrate the effectiveness of our approach on the tasks of unsupervised learning , classification , fine grained categorization , and zero - shot learning . 1 Introduction document : Part - based R - CNNs for Fine - grained Category Detection Semantic part localization can facilitate fine - grained categorization by explicitly isolating subtle appearance differences associated with specific object parts . Methods for pose - normalized representations have been proposed , but generally presume bounding box annotations at test time due to the difficulty of object detection . 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 . Facial recognition is the classic case of fine - grained recognition , and it is noteworthy that the best facial recognition methods jointly discover facial landmarks and extract features from those locations . Localizing the parts in an object is therefore central to establishing correspondence between object instances and discounting object pose variations and camera view position . However , on the much more challenging subset , our model degrades by . We hypothesize this to the fact that the sampled set during training , , comes from a random distribution of labels ( from ) , whereas the testing support set from contains similar classes , more akin to fine grained classification . Thus , we believe that if we adapted our training strategy to samples from fine grained sets of labels instead of sampling uniformly from the leafs of the ImageNet class tree , improvements could be attained . For car model verification , the fine - tuned model is employed as a feature extractor . subsection : Fine - Grained Classification We classify the car images into car models . We , therefore , show that the LSRO method is complementary to previous methods due to the regularization of the GAN data . subsection : Fine - grained Recognition Fine - grained recognition also faces the problem of a lack of training data and annotations . The number of layers for our models was set to be comparable to previously published results . On the fine - grained and binary classification tasks our 2 - layer LSTMN performs close to the best system T - CNN . Figure [ reference ] shows examples of intra - attention for sentiment words . An aggregation layer to aggregate the interaction signals for each class and make the final predictions . Considering that word - level encoders are well investigated in previous studies ( as mentioned in the Section 2 ) , and the target of this work is to learn the fine - grained classification signals , we only elaborate the interaction layer and aggregation layer in the following subsections . subsection : Interaction Layer How to achieve high accuracy on unsupervised satellite image classification is a key problem . For tackling this problem , based on the idea of partial transfer learning , we propose an novel end-to-end unsupervised classification method called coordinate partial adversarial domain adaptation ( CPADA ) for satellite image classification . Under the aid of a novel coordinate loss , our framework transfers relevant examples in the shared classes to promote performance , and ignore irrelevant ones in the specific classes to mitigate negative transfer . Remote sensing is widely used to monitor different resources available on earth surface , out of which the land used area monitoring is one of very important part . There are many techniques and methods are proposed for satellite image classification based on different theme . Here , in this paper theme of classification is land used and land area cover mapping using LISS-III satellite image . Because in GNNs the operation is carried out at a conceptual level , the components have unambiguous meaning , revealing how classification decisions are formed . In this paper , the interpretability of GNNs is exploited using a satellite image classification problem . We examine how land use classification using both spectral and non‐spectral information is expressed in GNN terms . This letter proposes a simple but effective approach to automatically learn a multilayer image feature for satellite image scene classification . Different from the hand-crafted features which are empirically designed but lack high generalization ability , the proposed approach can autonomously extract the data-dependent feature . Satellite image classification process involves grouping the image pixel values into meaningful categories . Several satellite image classification methods and techniques are available . Texture recognition is an important tool used for content-based image retrieval , face recognition , and satellite image classification applications . One of the most successful features for texture recognition is local binary patterns ( LBP ) , which computes local intensity differences for a pixel with respect to its neighbor pixels . Land Use information like buildings , roads and railway tracks are important features of urban infrastructure which crucially affect the life of people in cities . Recent developments in biologically inspired optimization techniques have motivated the researchers to explore the application of these techniques to the problem of satellite image feature classification . In this paper particle swarm optimization ( PSO ) along with the morphological operators is used for the identification of urban features like road and railway network , as well as land cover types , contained in the image . Few-shot sequence labeling faces a unique challenge compared with the other fewshot classification problems , owing to the necessity for modeling the dependencies between labels . Different domains often have different label sets , which makes it difficult to directly utilize the label dependencies learned from one domain in another domain . Such networks have been applied to few - shot image recognition [ reference ][ reference ] and learning " fast " reinforcement learning agents [ reference ][ reference ] . Our experiments show that our method outperforms the recurrent approach on fewshot classification . Furthermore , unlike these methods , our approach simply provides a good weight initialization and uses the same gradient descent update for both the learner and meta - update .
These methods highly depends on the preprocessing of brain images , including image rigid registration and segmentation . Motivated by the success of deep learning in image classification , this paper proposes a new classification framework based on combination of 2D convolutional neural networks ( CNN ) and recurrent neural networks ( RNNs ) , which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices . The 2D CNNs are built to capture the features of image slices while the gated recurrent unit ( GRU ) of RNN is cascaded to learn and integrate the inter-slice features for image classification .
The idea of making use of higher orders , or types , of fuzzy logic is to capture and represent uncertainty that is more complex . This paper is focused on image processing systems , which includes image segmentation , image filtering , image classification and edge detection . Various applications are presented where general type-2 fuzzy sets , interval type-2 fuzzy sets , and interval-value fuzzy sets are used ; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing .
Discriminant function is an algorithm for dividing line , the result is to provide an identification of the boundary equa tion , the equation is the discriminant function , so the discriminant function to describe the types of the dividing line between the specific form . This article is mainly based on the discriminant function model , the image classification problem is studied , through the example to illustrate the feasibility and effectiveness of the method of classification . The experimental results show that the dis criminant function model has a certain practical value , the image classification problem , the method of discriminant function good classification performance , accuracy and efficiency , finally , use the MATLAB graphical user interface(GUI ) design method , a good human-computer interaction system main interface .
In contrast to single images , image sets can capture a broad range of variations in the appearance of a single face or object . Recognition from these multiple images ( i.e. , image set classification ) has gained significant attention in the area of computer vision . Unlike many existing approaches , which assume that only the images in the same set affect each other , this work develops a group collaborative representation ( GCR ) model which makes no such assumption , and which can effectively determine the hidden structure among image sets .
Classification of a ship based on satellite imagery usually results differently depending on the type of polarization used in image generation . Also , the different ship ’s orientation in each image degrades the performance of image-based classification . Given these points , Sentinel-1 data also needs some methods to classify the type of ship .
The positive category is where the surface damage is present within the image , otherwise the image is classified as negative . This is an image-based classification . The ANN accepts image inputs that have been classified as positive by the ANN .
We compare the original random forest algorithm with their ensemble counterparts and discuss the results . Finally we deal the problem of image data classification with both supervised ( ensemble ) learning and unsupervised learning . We apply the algorithms developed in the thesis for this task .
Its features include : extending a keyword through bootstrapping construction . Aside from using bootstrapping construction to expand keywords and to classify images , we have also added a discriminative feature metric to increase the precision and recall rates of image classifying to our standards . Finally , we apply this technique on an image archives management system , named " Campus Image System " .
Image recording and binarization . Image analysis classification of the arrangement : Analysis of mixed structures ; teaching of an image analysis classifier . Applications .
A major issue concerning the design and implementation of an acquisition system for digital images of skin lesions is the ability of capturing reproducible images . The reproducibility is considered essential for image analysis classification and for the comparison of sequential images during follow-up studies . This paper describes a complete image acquisition system used for the collection of reproducible images of patients having melanoma and compares them with images displaying dysplastic nevus for diagnostic purposes .
Both techniques have their own pros and cons in finding the solutions of many applications . To create land cover thematic maps with higher accuracies then the image classification analysis is a big challenge in the remote sensing . However , the pixel based classification technique works only on spectral features and neglects the spatial features but in object based classification have ability to work on both spectral and spatial features .
Accuracy assessment should be a fundamental component of an image classification analysis and is typically undertaken following either a non-site- or a site-specific methodology . The assessment of classification accuracy is , however , often difficult , with many challenges associated with the ground data typically required .
A with classifying the sound segment of the video at the sound segment classification unit ( 103 ) , by classifying the image segmentation of the image in the image segment classification unit ( 104 ) , classification results in a plurality generator ( 105 ) , a plurality of generating at least one sound segment classification results and a plurality of image segment classification result , the classification results pair generation unit at ( 106 ) , generate a plurality of classification results pair of these classification results , as candidates of the optimum segment classification result for the image and classification by comparing the classification result output unit the classification results between scores for each classification result pair calculated result between score calculator ( 107 ) ( 108 ) , a good correspondence take such sound segment classification result and the image segment and it outputs the classification result . Thus , without prior adjusting the parameters , a plurality of sound segment and a plurality of image segments included in the video , it can be accurately classified by the object .
Conventional image categorization techniques primarily rely on low-level visual cues . In this paper , we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification . Specific to each image category , a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning [ 1 ] .
We address this issue by proposing an effective global prior representation . Global average pooling is a good baseline model as the global contextual prior , which is commonly used in image classification tasks . In , it was successfully applied to semantic segmentation .
We used the FCN - 8s architecture of [ reference ] as the first part of our network , which provides unary potentials to the CRF . This network is based on the VGG - 16 network [ reference ] but has been restructured to perform pixel - wise prediction instead of image classification . The complete architecture of our network , including the FCN8s part can be found in the appendix .
The initial convolutional layers of the network extract features from the image while the fully connected layers predict the output probabilities and coordinates . Our network architecture is inspired by the GoogLeNet model for image classification . Our network has 24 convolutional layers followed by 2 fully connected layers .
In this paper , we propose a novel deep network for WSOD . Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning ( MIL ) , our strategy generates proposal clusters to learn refined instance classifiers by an iterative process . The proposals in the same cluster are spatially adjacent and associated with the same object .
Semantic image segmentation is an essential component of modern autonomous driving systems , as an accurate understanding of the surrounding scene is crucial to navigation and action planning . Current state - of - the - art approaches in semantic image segmentation rely on pretrained networks that were initially developed for classifying images as a whole . While these networks exhibit outstanding recognition performance ( i.e. , what is visible ? ) , they lack localization accuracy ( i.e. , where precisely is something located ? ) .
In VggNet - PCA + LSTM , PCA is applied to decrease the feature dimension from 4096 to 1000 . VggNet + ft + LSTM applies a VggNet that has been fine - tuned on the target dataset , based on the task of image - attributes classification . Our Approaches :
On a traffic sign recognition benchmark it outperforms humans by a factor of two . We also improve the state - of - the - art on a plethora of common image classification benchmarks . 04 - 12
We believe that the presented work is a step towards dedicated architectures for dense prediction that are not constrained by image classification precursors . As new sources of data become available , future architectures may be trained densely end - to - end , removing the need for pre - training on image classification datasets . This may enable architectural simplification and unification .
Convolutional neural networks ( CNNs ) are a special type of artificial neural networks that learn a hierarchy of increasingly complex features by successive convolution , pooling and non - linear activation operations . Originally designed for image recognition and classification , CNNs are now commonly used in semantic image segmentation . A naive approach follows a sliding - window strategy , where regions defined by the window are processed independently .
Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis . Due to the nuclei tiny size , significant inter-/intra-class variances , as well as the inferior image quality , previous automated methods would easily suffer from limited accuracy and robustness .
It is common for words to be POS-tagged , where each word is associated with a tag that identifies its word class ( and perhaps more ) . The set of classes used , the tagset , may be akin to the conventional parts of speech but usually reflects a rather more fine-grained classification . For example , the CLAWS-5 tagset used in the British National Corpus ( BNC ) has 57 simple word class tags , and some others have many more still .
This thesis tackles fine-grained image recognition , the task of sub-category or species classification . It explores general methods to improve fine-grained image classification including the use of generative models and deep convolutional neural networks leading to novel models such as a Mixture of deep convolution neural networks . This work led to 9 peer reviewed publications and a Best Paper Award .
In this work , we attempt to classify commodities in containers with HS(harmonized system ) codes , which is a challenging task due to the large number of categories in HS codes and its hierarchical structure based on a product 's composition and economic activity . To tackle this problem , in this paper we propose an ensemble model which incorporates fine-grained image categorization , data analysis on cargo manifests , and human-in-the-loop paradigm . By employing deep learning , we train a triplet network for fine-grained image categorization .
: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification . Data augmentation is usually adopted to increase the amount of training data , prevent overfitting and improve the performance of deep models .
The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task . Compared with the CNN method in image classification , the model is proficient in fine-grained classification problem and has a better robustness due to its mechanism of multi-glance and visual attention . We evaluate the model on vehicle dataset , where its performance exceeds CNN baseline on image classification .
It also outperforms DBSCAN and its recent variation DSets-DBSCAN . We have chosen species recognition ( bird and butterflies ) as a representative fine-grained image categorization problem , though the proposed algorithm is a general approach applicable to other similar tasks . We also introduce a new benchmark fine-grained image dataset , that of Indian endemic butterfly species ( Titli.vl ) , which is available through the corresponding author .
There are many types of butterflies , and the research on classification of butterfly species is of great significance in practical work such as environmental protection and control of agricultural and forest pests . Butterfly classification is a fine-grained image classification problem that is more difficult than generic image classification . Common butterfly photos are mostly specimen photos ( indoor photos ) and ecological photos ( outdoor photos/natural images ) .
Hence , we introduce a generic framework to overcome the issue , integrating the graph convolutional network ( GCN ) and deep structured feature embedding ( DSFE ) into an end-to-end workflow . Furthermore , instead of using a classic graph convolutional neural network , we propose a gated graph convolutional network , which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification . Taking the semantic segmentation of building footprints as a practical example , we compared different feature embedding architectures and graph neural networks .
Our k - means style objective is easy to optimize and flexible , supporting various forms of clustering , such as sample clustering , spatial clustering , as well as co - clustering . We demonstrate the effectiveness of our approach on the tasks of unsupervised learning , classification , fine grained categorization , and zero - shot learning . 1 Introduction
document : Part - based R - CNNs for Fine - grained Category Detection Semantic part localization can facilitate fine - grained categorization by explicitly isolating subtle appearance differences associated with specific object parts . Methods for pose - normalized representations have been proposed , but generally presume bounding box annotations at test time due to the difficulty of object detection .
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 . Facial recognition is the classic case of fine - grained recognition , and it is noteworthy that the best facial recognition methods jointly discover facial landmarks and extract features from those locations . Localizing the parts in an object is therefore central to establishing correspondence between object instances and discounting object pose variations and camera view position .
However , on the much more challenging subset , our model degrades by . We hypothesize this to the fact that the sampled set during training , , comes from a random distribution of labels ( from ) , whereas the testing support set from contains similar classes , more akin to fine grained classification . Thus , we believe that if we adapted our training strategy to samples from fine grained sets of labels instead of sampling uniformly from the leafs of the ImageNet class tree , improvements could be attained .
For car model verification , the fine - tuned model is employed as a feature extractor . subsection : Fine - Grained Classification We classify the car images into car models .
We , therefore , show that the LSRO method is complementary to previous methods due to the regularization of the GAN data . subsection : Fine - grained Recognition Fine - grained recognition also faces the problem of a lack of training data and annotations .
The number of layers for our models was set to be comparable to previously published results . On the fine - grained and binary classification tasks our 2 - layer LSTMN performs close to the best system T - CNN . Figure [ reference ] shows examples of intra - attention for sentiment words .
An aggregation layer to aggregate the interaction signals for each class and make the final predictions . Considering that word - level encoders are well investigated in previous studies ( as mentioned in the Section 2 ) , and the target of this work is to learn the fine - grained classification signals , we only elaborate the interaction layer and aggregation layer in the following subsections . subsection : Interaction Layer
How to achieve high accuracy on unsupervised satellite image classification is a key problem . For tackling this problem , based on the idea of partial transfer learning , we propose an novel end-to-end unsupervised classification method called coordinate partial adversarial domain adaptation ( CPADA ) for satellite image classification . Under the aid of a novel coordinate loss , our framework transfers relevant examples in the shared classes to promote performance , and ignore irrelevant ones in the specific classes to mitigate negative transfer .
Remote sensing is widely used to monitor different resources available on earth surface , out of which the land used area monitoring is one of very important part . There are many techniques and methods are proposed for satellite image classification based on different theme . Here , in this paper theme of classification is land used and land area cover mapping using LISS-III satellite image .
Because in GNNs the operation is carried out at a conceptual level , the components have unambiguous meaning , revealing how classification decisions are formed . In this paper , the interpretability of GNNs is exploited using a satellite image classification problem . We examine how land use classification using both spectral and non‐spectral information is expressed in GNN terms .
This letter proposes a simple but effective approach to automatically learn a multilayer image feature for satellite image scene classification . Different from the hand-crafted features which are empirically designed but lack high generalization ability , the proposed approach can autonomously extract the data-dependent feature .
Satellite image classification process involves grouping the image pixel values into meaningful categories . Several satellite image classification methods and techniques are available .
Texture recognition is an important tool used for content-based image retrieval , face recognition , and satellite image classification applications . One of the most successful features for texture recognition is local binary patterns ( LBP ) , which computes local intensity differences for a pixel with respect to its neighbor pixels .
Land Use information like buildings , roads and railway tracks are important features of urban infrastructure which crucially affect the life of people in cities . Recent developments in biologically inspired optimization techniques have motivated the researchers to explore the application of these techniques to the problem of satellite image feature classification . In this paper particle swarm optimization ( PSO ) along with the morphological operators is used for the identification of urban features like road and railway network , as well as land cover types , contained in the image .
Few-shot sequence labeling faces a unique challenge compared with the other fewshot classification problems , owing to the necessity for modeling the dependencies between labels . Different domains often have different label sets , which makes it difficult to directly utilize the label dependencies learned from one domain in another domain .
Such networks have been applied to few - shot image recognition [ reference ][ reference ] and learning " fast " reinforcement learning agents [ reference ][ reference ] . Our experiments show that our method outperforms the recurrent approach on fewshot classification . Furthermore , unlike these methods , our approach simply provides a good weight initialization and uses the same gradient descent update for both the learner and meta - update .
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IGARSS ACM Multimedia 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence 2012 IEEE Conference on Computer Vision and Pattern Recognition ICLR Comput. Medical Imaging Graph. CVPR Workshops IVCNZ NIPS ECCV NIPS 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) EMNLP AAAI SIU ICML
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