Even though multiple approaches have been taken towards the minimization of the execution time and errors in the results , most of the time the number of parameters of the networks is neither taken into consideration nor optimized . Inspired on the Squeeze-Nets developed for image recognition , we developed a Stereo Matching Squeeze neural network architecture capable of providing disparity maps with a highly reduced network size without a significant impact on quality and execution time compared with state of the art architectures . In addition , with the purpose of improving the quality of the solution and get solutions closer to real time , an extra refinement module is proposed and several tests are performed using different input size reductions . In the image recognition problem , it is very important how we represent the image . Considering this , we propose a new representational method of images based on the stability in scale-space . The paper presents recent results in establishing existence conditions of a class of efficient algorithms for image recognition problem including the algorithm that correctly solves this problem . The proposed method for checking on satisfiability of these conditions is based on the new definition of image equivalence introduced for a special formulation of an image recognition problem . It is shown that the class of efficient algorithms based on estimate calculation contains the correct algorithm in its algebraic closure . A new method for image object recognition is proposed . The complicated relation between the visual features and the recognizing result are modeled using evidence theory in the proposed method . Provided is an image processing device whereby it is possible to perform an image recognition process for all objects to be recognized , and to reduce load in image intake and transference . The image processing device comprises : an image intake unit which takes in image information which is captured with an image capture element ; a process region setting unit which sets a plurality of process regions with respect to the image information ; a process order/frequency determination unit which determines at least one of a sequence , a frequency , or a timing of taking in each instance of the image information of the set plurality of process regions , and at least one of a sequence , a frequency , or a timing of the processing of each instance of the image information ; and an image processing unit which takes in the image information for each process region at the determined sequence , frequency , or timing , and carries out image processing on the image information at the sequence , frequency , or timing which is determined . An approach to the control of multisensor image processing and recognition based on a suitable representation of control knowledge in symbolic form is presented . A hierarchical organization of control knowledge , corresponding to a decomposition of the image recognition process into subprocesses , is proposed . The knowledge for the control of the low-level and high-level phases is described in detail . This paper discusses how to design 3D LSI chip stacking system with cooperative HW/SW . As a case study , a mapping design for image recognition processing is evaluated , in which partial processing is mapped onto 3D-SCSS . Aimed at the second generation of curvelet transform algorithm in the process of image recognition processing , the existing image edge " ringing"effect and image distortion as a result of the nature of the " wedge base " , the second generation curvelet weighted improved algorithm is proposed and the implementation process of image recognition . And respectively compared the ORL with Yale image simulation experiment , proves that it is better in the recognition than traditional wavelet weighted bi-directional twodimensional principal component analysis algorithm , so as to verify the feasibility and effectiveness of the algorithm on image recognition processing . Recently , Convolutional Neural Networks ( ConvNets ) have shown promising performances in many computer vision tasks , especially image-based recognition . How to effectively use ConvNets for video-based recognition is still an open problem . With the recent advances in the programmability and performance of mobile Graphics Processing Units ( GPUs ) , General-Purpose Graphics Processing Unit ( GPGPU ) technologies have become available even in mobile devices such as smartphones and tablets . Among the available GPGPU technologies for mobile devices , Open Computing Language ( OpenCL ) and RenderScript are used to accelerate applications in various fields such as computer graphics , image processing/recognition , and computer vision . For example , these technologies are used for detecting collisions and edges , processing data from a camera , recognizing an object in an image , processing the images stored on a device , and accelerating the drawing of an image when live wallpaper is used in Android-based devices . He has over 30 published papers and/or technical presentations while spearheading over 40 international scientific and engineering conferences/workshops as a steering committee member while assigned in Europe . Professor Santiago has experience in many engineering disciplines and missions including : control and modeling of large flexible space structures , communications system , electro-optics , high-energy lasers , missile seekers/sensors for precision guided munitions , image processing/recognition , information technologies , space , air and missile warning , missile defense , and homeland defense . The invention relates to a self-adaption wavelet neural network abnormity detection and fault diagnosis classification system and method , which can be applied to the fields , such as economic management abnormity detection , image recognition analysis , video retrieval , audio retrieval , signal abnormity detection , safety detection , and the like . The system comprises the following seven parts : an acquisition device , a transmitter device , an A/D ( Analog/Digital ) conversion device , a self-adaption wavelet neural network abnormity detection and fault diagnosis classification processor , a display interaction device , an abnormity alarm device and an abnormity processing device . The invention provides a ground object information collection device and a program thereof , a self-driving position recognition device and a navigation device , which can quickly collect the ground object information after construction when the ground object position shifts or the ground object category alters owing to construction . The devices are provided with a ground object image recognition unit carrying out image recognition processing to the ground objects included in the image information of the vehicle surroundings , a construction information acquisition unit for acquiring the construction information in the construction zone , a construction storage unit for storing the acquired construction information , a construction completion judging unit for judging whether the construction indicated by the construction information completes when the vehicle drives on the roads corresponding to the constriction zone related to the stored construction information , and a ground object study unit for enabling the ground object recognition unit to carry out image recognition processing to the ground object in the construction zone related to the construction information after the construction is judged to complete and generating study ground object information including the position information and attribution information of the recognized ground object based on the image recognition result and the vehicle position information . When training only on source samples , the effect of the BN is not clear as in Tables 1 . However , in all image recognition experiments , the effect of BN in our method is clear ; at the same time , the effect of our method is also clear when we do not use BN in the network architecture . The effect of the weight constraint is obvious in MNIST→SVHN . First , in this work we would like to focus on analyzing the effectiveness and performance of the aggregation module with the attention mechanism . Despite the huge success of applying deep CNN in image - based face recognition task , little attention has been drawn to CNN feature aggregation to our knowledge . Second , training a deep CNN usually necessitates a large volume of labeled data . We re - evaluate the state of the art for object recognition from small images with convolutional networks , questioning the necessity of different components in the pipeline . We find that max - pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks . 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 ) . License plate recognition ( LPR ) is an important part of intelligent transportation management ( ITM ) . All of the information of the vehicle can be acquired by recognizing the license plate which is unique to the owner . This paper researches on the image pre-processing algorithm of Vehicle License Plate Recognition ( VLPR ) , introduces the significance of the license plate recognition technology , the methods of image noise reduction , binary image and the realization of image preprocessing algorithms in experimental system . Through the test process of image processing , simulation proves that the paper in the image processing on the validity of license plate recognition . Problem statement : License plate detection and recognition is an imag e-processing technique used to identify a vehicle by its license plate . This notable technology has got multiple applications in various traffic and security cases . License plate detection and recognition are mostly studied on automobiles but only few on motorcycles . As motorcycles are becoming popular for local transportation and environmental friendliness , the demands for license plate recognition have been increasing in recent years . An enhanced Geometrical Feature Topological Analysis is proposed as a feature extractor and support vector machine is used as the classification technique . The proposed techniques and known feature extractors were used to justify its robustness for license plate recognition problem in precise . Previous research in the same domain , has applied straight pixels as the features . This paper presents a solution for the license plate recognition problem under low illumination on the basis of in-depth analysis of the characteristic of license plate images . In recent years , with the development of technologies such as optics , computers and pattern recognition , license plate recognition technology is improved constantly , but the accuracy of license plate recognition under low illumination environment still needs to be improved . The method of the present and performs the capture of the violation vehicle to capture a two-way manner , to capture and present information and license plate number recognition capture information sent to the control system can simultaneously capture the identification information according to the information and to capture license plate number , the information may be identified according to a capture orientation , then when the identification information does not identify the other direction according to capture . The camera capture system includes at least one embodiment of the present photographing to the camera and to shoot a pair of the camera , and a control for capture and capture the license plate number recognition and analysis information control system , the present invention can be taken to simultaneously front and rear license plates , even in the case after the license plate obscured by malicious , can identify the license plate number , can be mainly used for road traffic monitoring and management . The first step of the license plate automatic recognition is the license plate location , therefore , the license plate location method is very important to the license plate automatic recognition . A license plate location method combining the license plate location method based on the HSV color space and the license plate location method based on the boundary detection is presented in this paper . To a large extent , Intelligent transport system ( ITS ) is proved to resolve this problem . License plate automatic recognition ( LPR ) is the key part of the ITS . Plate location , character segmentation and character identification are the three parts of LPR . This paper put forward improvement a new methods of license plate image recognition based on the edge detection and mathematical morphology . First , it will be the collected color image into gray image , then use Sobel operators are added after the edge detection , and then used the morphology of the corrosion processing , get the outline of smooth image , and then to X direction positioning and Y direction positioning and regional correction for license plate area , through to the license plate image binarization and morphological filtering the license plate characters to effective segmentation out , then using the method of template matching license plate character recognition . Conventional license plate detection and recognition algorithms are challenged in low-resolution video processing . In this paper , we introduce the theory and methodology of GAN and transfer learning , which are applied to deal with the license plate image recognition under several complex environments . The experimental results show that the method adopted in this paper has higher recognition rate and robustness . License plate location is one of the key link in the license plate recognition process . Whether the plate location is successful and how accurate is the location decide directly the recognition and the effects in the latter part . Compared with the traditional algorithm , our method not only speeds up the recognition efficiency , but also obtains more robust results . Therefore , the proposed approach can provide a technical support for the practical application of license plate recognizing . We give limit theorems for the learned parameters , where we fully specify the limiting distribution so that confidence intervals can be computed . On real tasks including generalizing to unknown subpopulations , fine-grained recognition , and providing good tail performance , the distributionally robust approach often exhibits improved performance . However , in real applications , the part and/or bounding box annotations may not exist . This makes fine-grained recognition a challenging problem . In this paper , we propose a jointly trained Convolutional Neural Network ( CNN ) architecture to solve the fine-grained recognition problem without using part and bounding box information . Recent studies have obtained superior performance in image recognition tasks by using , as an image representation , the fully connected layer activations of Convolutional Neural Networks ( CNN ) trained with various kinds of images . However , the CNN representation is not very suitable for fine-grained image recognition tasks involving food image recognition . For improving performance of the CNN representation in food image recognition , we propose a novel image representation that is comprised of the covariances of convolutional layer feature maps . However , fine-grained recognition remains a big challenge due to the lack of large-scale well labeled dataset to train the network . In this paper , we study a deep network based method for fine-grained image recognition by utilizing the click-through logs from search engines . We use both click times and probability values to filter out the noise in click-through logs . Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition . Fine-grained visual recognition typically depends on modeling subtle difference from object parts . In this paper , we will provide CPSS service for fine-grained recognition . Fine-grained visual recognition is a hot but challenging research in computer vision that aims to recognize object subcategories . The reason why it is challenging is that it extremely depends on the subtle discriminative features of local parts . To achieve this , we develop a unified framework based on a mixture of experts . Due to limited data available for the fine-grained recognition problem , it is not feasible to learn diverse experts by using a data division strategy . To tackle the problem , we promote diversity among experts by combing an expert gradually-enhanced learning strategy and a Kullback – Leibler divergence based constraint . We address the fine-grained image recognition problem using user click data , wherein each image is represented as a semantical query-click feature vector . Usually , the query set obtained from search engines is large-scale and redundant . Humans are capable of learning a new fine-grained concept with very little supervision , e.g. , few exemplary images for a species of bird , yet our best deep learning systems need hundreds or thousands of labeled examples . In this paper , we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting , termed few-shot fine-grained recognition ( FSFG ) . The task of FSFG requires the learning systems to build classifiers for the novel fine-grained categories from few examples ( only one or less than five ) . The output of each block is further obtained by aggregating intra-group bilinear features , with residuals from the entire input features . We found that the proposed network achieves new state-of-the-art in several fine-grained image recognition benchmarks , including CUB-Bird , Stanford-Car , and FGVC-Aircraft . It was created to perform taxonomic identification of bird species by a single feather , which can be applied in amateur and professional ornithology . FeatherV1 is the first publicly available bird 's plumage dataset for machine learning , and it can raise interest for a new task in fine-grained visual recognition domain . The latest version of the dataset can be downloaded at https://github.com/feathers-dataset/feathersv1-dataset . In this paper , we tackle the FINE-GRAINED VISUAL RECOGNITION problem by proposing a deep multi-context framework . We employ deep Convolutional Neural Networks to model features of objects in images . In the context of fine-grained visual categorization , the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy . In this paper , we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts . Based on manually-labeled strong part annotations , the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that encodes object-level and part-level cues simultaneously . For instance , we need to rely on the big table as context to better tell whether the small black blob placed on it is a cup or a pen holder . Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Image classification has advanced significantly in recent years with the availability of large - scale image sets . However , fine - grained classification remains a major challenge due to the annotation cost of large numbers of fine - grained categories . This project shows that compelling classification performance can be achieved on such categories even without labeled training data . subsection : Fine - grained Recognition Fine - grained recognition also faces the problem of a lack of training data and annotations . To further test the effectiveness of our method , we provide results on the CUB - 200 - 2011 dataset . Our method learns detectors and part models and enforces learned geometric constraints between parts and with the object frame . Our experimental results demonstrate that even with a very strong feature representation and object detection system , it is highly beneficial to additionally model an object ’s pose by means of parts for the difficult task of fine - grained discrimination between categories with high semantic similarity . In future extensions of this work , we will consider methods which jointly model at training time the object category and each of its parts and deformation costs .
Even though multiple approaches have been taken towards the minimization of the execution time and errors in the results , most of the time the number of parameters of the networks is neither taken into consideration nor optimized . Inspired on the Squeeze-Nets developed for image recognition , we developed a Stereo Matching Squeeze neural network architecture capable of providing disparity maps with a highly reduced network size without a significant impact on quality and execution time compared with state of the art architectures . In addition , with the purpose of improving the quality of the solution and get solutions closer to real time , an extra refinement module is proposed and several tests are performed using different input size reductions .
In the image recognition problem , it is very important how we represent the image . Considering this , we propose a new representational method of images based on the stability in scale-space .
The paper presents recent results in establishing existence conditions of a class of efficient algorithms for image recognition problem including the algorithm that correctly solves this problem . The proposed method for checking on satisfiability of these conditions is based on the new definition of image equivalence introduced for a special formulation of an image recognition problem . It is shown that the class of efficient algorithms based on estimate calculation contains the correct algorithm in its algebraic closure .
A new method for image object recognition is proposed . The complicated relation between the visual features and the recognizing result are modeled using evidence theory in the proposed method .
Provided is an image processing device whereby it is possible to perform an image recognition process for all objects to be recognized , and to reduce load in image intake and transference . The image processing device comprises : an image intake unit which takes in image information which is captured with an image capture element ; a process region setting unit which sets a plurality of process regions with respect to the image information ; a process order/frequency determination unit which determines at least one of a sequence , a frequency , or a timing of taking in each instance of the image information of the set plurality of process regions , and at least one of a sequence , a frequency , or a timing of the processing of each instance of the image information ; and an image processing unit which takes in the image information for each process region at the determined sequence , frequency , or timing , and carries out image processing on the image information at the sequence , frequency , or timing which is determined .
An approach to the control of multisensor image processing and recognition based on a suitable representation of control knowledge in symbolic form is presented . A hierarchical organization of control knowledge , corresponding to a decomposition of the image recognition process into subprocesses , is proposed . The knowledge for the control of the low-level and high-level phases is described in detail .
This paper discusses how to design 3D LSI chip stacking system with cooperative HW/SW . As a case study , a mapping design for image recognition processing is evaluated , in which partial processing is mapped onto 3D-SCSS .
Aimed at the second generation of curvelet transform algorithm in the process of image recognition processing , the existing image edge " ringing"effect and image distortion as a result of the nature of the " wedge base " , the second generation curvelet weighted improved algorithm is proposed and the implementation process of image recognition . And respectively compared the ORL with Yale image simulation experiment , proves that it is better in the recognition than traditional wavelet weighted bi-directional twodimensional principal component analysis algorithm , so as to verify the feasibility and effectiveness of the algorithm on image recognition processing .
Recently , Convolutional Neural Networks ( ConvNets ) have shown promising performances in many computer vision tasks , especially image-based recognition . How to effectively use ConvNets for video-based recognition is still an open problem .
With the recent advances in the programmability and performance of mobile Graphics Processing Units ( GPUs ) , General-Purpose Graphics Processing Unit ( GPGPU ) technologies have become available even in mobile devices such as smartphones and tablets . Among the available GPGPU technologies for mobile devices , Open Computing Language ( OpenCL ) and RenderScript are used to accelerate applications in various fields such as computer graphics , image processing/recognition , and computer vision . For example , these technologies are used for detecting collisions and edges , processing data from a camera , recognizing an object in an image , processing the images stored on a device , and accelerating the drawing of an image when live wallpaper is used in Android-based devices .
He has over 30 published papers and/or technical presentations while spearheading over 40 international scientific and engineering conferences/workshops as a steering committee member while assigned in Europe . Professor Santiago has experience in many engineering disciplines and missions including : control and modeling of large flexible space structures , communications system , electro-optics , high-energy lasers , missile seekers/sensors for precision guided munitions , image processing/recognition , information technologies , space , air and missile warning , missile defense , and homeland defense .
The invention relates to a self-adaption wavelet neural network abnormity detection and fault diagnosis classification system and method , which can be applied to the fields , such as economic management abnormity detection , image recognition analysis , video retrieval , audio retrieval , signal abnormity detection , safety detection , and the like . The system comprises the following seven parts : an acquisition device , a transmitter device , an A/D ( Analog/Digital ) conversion device , a self-adaption wavelet neural network abnormity detection and fault diagnosis classification processor , a display interaction device , an abnormity alarm device and an abnormity processing device .
The invention provides a ground object information collection device and a program thereof , a self-driving position recognition device and a navigation device , which can quickly collect the ground object information after construction when the ground object position shifts or the ground object category alters owing to construction . The devices are provided with a ground object image recognition unit carrying out image recognition processing to the ground objects included in the image information of the vehicle surroundings , a construction information acquisition unit for acquiring the construction information in the construction zone , a construction storage unit for storing the acquired construction information , a construction completion judging unit for judging whether the construction indicated by the construction information completes when the vehicle drives on the roads corresponding to the constriction zone related to the stored construction information , and a ground object study unit for enabling the ground object recognition unit to carry out image recognition processing to the ground object in the construction zone related to the construction information after the construction is judged to complete and generating study ground object information including the position information and attribution information of the recognized ground object based on the image recognition result and the vehicle position information .
When training only on source samples , the effect of the BN is not clear as in Tables 1 . However , in all image recognition experiments , the effect of BN in our method is clear ; at the same time , the effect of our method is also clear when we do not use BN in the network architecture . The effect of the weight constraint is obvious in MNIST→SVHN .
First , in this work we would like to focus on analyzing the effectiveness and performance of the aggregation module with the attention mechanism . Despite the huge success of applying deep CNN in image - based face recognition task , little attention has been drawn to CNN feature aggregation to our knowledge . Second , training a deep CNN usually necessitates a large volume of labeled data .
We re - evaluate the state of the art for object recognition from small images with convolutional networks , questioning the necessity of different components in the pipeline . We find that max - pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks . 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 ) .
License plate recognition ( LPR ) is an important part of intelligent transportation management ( ITM ) . All of the information of the vehicle can be acquired by recognizing the license plate which is unique to the owner .
This paper researches on the image pre-processing algorithm of Vehicle License Plate Recognition ( VLPR ) , introduces the significance of the license plate recognition technology , the methods of image noise reduction , binary image and the realization of image preprocessing algorithms in experimental system . Through the test process of image processing , simulation proves that the paper in the image processing on the validity of license plate recognition .
Problem statement : License plate detection and recognition is an imag e-processing technique used to identify a vehicle by its license plate . This notable technology has got multiple applications in various traffic and security cases .
License plate detection and recognition are mostly studied on automobiles but only few on motorcycles . As motorcycles are becoming popular for local transportation and environmental friendliness , the demands for license plate recognition have been increasing in recent years .
An enhanced Geometrical Feature Topological Analysis is proposed as a feature extractor and support vector machine is used as the classification technique . The proposed techniques and known feature extractors were used to justify its robustness for license plate recognition problem in precise . Previous research in the same domain , has applied straight pixels as the features .
This paper presents a solution for the license plate recognition problem under low illumination on the basis of in-depth analysis of the characteristic of license plate images . In recent years , with the development of technologies such as optics , computers and pattern recognition , license plate recognition technology is improved constantly , but the accuracy of license plate recognition under low illumination environment still needs to be improved .
The method of the present and performs the capture of the violation vehicle to capture a two-way manner , to capture and present information and license plate number recognition capture information sent to the control system can simultaneously capture the identification information according to the information and to capture license plate number , the information may be identified according to a capture orientation , then when the identification information does not identify the other direction according to capture . The camera capture system includes at least one embodiment of the present photographing to the camera and to shoot a pair of the camera , and a control for capture and capture the license plate number recognition and analysis information control system , the present invention can be taken to simultaneously front and rear license plates , even in the case after the license plate obscured by malicious , can identify the license plate number , can be mainly used for road traffic monitoring and management .
The first step of the license plate automatic recognition is the license plate location , therefore , the license plate location method is very important to the license plate automatic recognition . A license plate location method combining the license plate location method based on the HSV color space and the license plate location method based on the boundary detection is presented in this paper .
To a large extent , Intelligent transport system ( ITS ) is proved to resolve this problem . License plate automatic recognition ( LPR ) is the key part of the ITS . Plate location , character segmentation and character identification are the three parts of LPR .
This paper put forward improvement a new methods of license plate image recognition based on the edge detection and mathematical morphology . First , it will be the collected color image into gray image , then use Sobel operators are added after the edge detection , and then used the morphology of the corrosion processing , get the outline of smooth image , and then to X direction positioning and Y direction positioning and regional correction for license plate area , through to the license plate image binarization and morphological filtering the license plate characters to effective segmentation out , then using the method of template matching license plate character recognition .
Conventional license plate detection and recognition algorithms are challenged in low-resolution video processing . In this paper , we introduce the theory and methodology of GAN and transfer learning , which are applied to deal with the license plate image recognition under several complex environments . The experimental results show that the method adopted in this paper has higher recognition rate and robustness .
License plate location is one of the key link in the license plate recognition process . Whether the plate location is successful and how accurate is the location decide directly the recognition and the effects in the latter part .
Compared with the traditional algorithm , our method not only speeds up the recognition efficiency , but also obtains more robust results . Therefore , the proposed approach can provide a technical support for the practical application of license plate recognizing .
We give limit theorems for the learned parameters , where we fully specify the limiting distribution so that confidence intervals can be computed . On real tasks including generalizing to unknown subpopulations , fine-grained recognition , and providing good tail performance , the distributionally robust approach often exhibits improved performance .
However , in real applications , the part and/or bounding box annotations may not exist . This makes fine-grained recognition a challenging problem . In this paper , we propose a jointly trained Convolutional Neural Network ( CNN ) architecture to solve the fine-grained recognition problem without using part and bounding box information .
Recent studies have obtained superior performance in image recognition tasks by using , as an image representation , the fully connected layer activations of Convolutional Neural Networks ( CNN ) trained with various kinds of images . However , the CNN representation is not very suitable for fine-grained image recognition tasks involving food image recognition . For improving performance of the CNN representation in food image recognition , we propose a novel image representation that is comprised of the covariances of convolutional layer feature maps .
However , fine-grained recognition remains a big challenge due to the lack of large-scale well labeled dataset to train the network . In this paper , we study a deep network based method for fine-grained image recognition by utilizing the click-through logs from search engines . We use both click times and probability values to filter out the noise in click-through logs .
Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition . Fine-grained visual recognition typically depends on modeling subtle difference from object parts .
In this paper , we will provide CPSS service for fine-grained recognition . Fine-grained visual recognition is a hot but challenging research in computer vision that aims to recognize object subcategories . The reason why it is challenging is that it extremely depends on the subtle discriminative features of local parts .
To achieve this , we develop a unified framework based on a mixture of experts . Due to limited data available for the fine-grained recognition problem , it is not feasible to learn diverse experts by using a data division strategy . To tackle the problem , we promote diversity among experts by combing an expert gradually-enhanced learning strategy and a Kullback – Leibler divergence based constraint .
We address the fine-grained image recognition problem using user click data , wherein each image is represented as a semantical query-click feature vector . Usually , the query set obtained from search engines is large-scale and redundant .
Humans are capable of learning a new fine-grained concept with very little supervision , e.g. , few exemplary images for a species of bird , yet our best deep learning systems need hundreds or thousands of labeled examples . In this paper , we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting , termed few-shot fine-grained recognition ( FSFG ) . The task of FSFG requires the learning systems to build classifiers for the novel fine-grained categories from few examples ( only one or less than five ) .
The output of each block is further obtained by aggregating intra-group bilinear features , with residuals from the entire input features . We found that the proposed network achieves new state-of-the-art in several fine-grained image recognition benchmarks , including CUB-Bird , Stanford-Car , and FGVC-Aircraft .
It was created to perform taxonomic identification of bird species by a single feather , which can be applied in amateur and professional ornithology . FeatherV1 is the first publicly available bird 's plumage dataset for machine learning , and it can raise interest for a new task in fine-grained visual recognition domain . The latest version of the dataset can be downloaded at https://github.com/feathers-dataset/feathersv1-dataset .
In this paper , we tackle the FINE-GRAINED VISUAL RECOGNITION problem by proposing a deep multi-context framework . We employ deep Convolutional Neural Networks to model features of objects in images .
In the context of fine-grained visual categorization , the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy . In this paper , we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts . Based on manually-labeled strong part annotations , the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that encodes object-level and part-level cues simultaneously .
For instance , we need to rely on the big table as context to better tell whether the small black blob placed on it is a cup or a pen holder . Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection .
Image classification has advanced significantly in recent years with the availability of large - scale image sets . However , fine - grained classification remains a major challenge due to the annotation cost of large numbers of fine - grained categories . This project shows that compelling classification performance can be achieved on such categories even without labeled training data .
subsection : Fine - grained Recognition Fine - grained recognition also faces the problem of a lack of training data and annotations . To further test the effectiveness of our method , we provide results on the CUB - 200 - 2011 dataset .
Our method learns detectors and part models and enforces learned geometric constraints between parts and with the object frame . Our experimental results demonstrate that even with a very strong feature representation and object detection system , it is highly beneficial to additionally model an object ’s pose by means of parts for the difficult task of fine - grained discrimination between categories with high semantic similarity . In future extensions of this work , we will consider methods which jointly model at training time the object category and each of its parts and deformation costs .
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CIARP CIVR MCSoC ACM Multimedia ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ICME Workshops ISC2 MMM (1) ICCV ICIMCS NeurIPS ICME Workshops CVPR IEEE transactions on pattern analysis and machine intelligence 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) ECCV
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