G. Schaefer, B. Krawczyk, M. E. Celebi, H. Iyatomi
Malignant melanoma, the deadliest form of skin cancer, is one of the most rapidly increasing cancers in the world. Early diagnosis is crucial, since if detected early, it can be cured through a simple excision. In this paper, we present an effective approach to melanoma classification from dermoscopic images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. Shape features are then extracted from this border, while colour and texture features are obtained based on a division of the image into clinically significant regions. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and the use of our classifier ensemble to lead to statistically better recognition performance.
{"title":"Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning","authors":"G. Schaefer, B. Krawczyk, M. E. Celebi, H. Iyatomi","doi":"10.1109/ACPR.2013.102","DOIUrl":"https://doi.org/10.1109/ACPR.2013.102","url":null,"abstract":"Malignant melanoma, the deadliest form of skin cancer, is one of the most rapidly increasing cancers in the world. Early diagnosis is crucial, since if detected early, it can be cured through a simple excision. In this paper, we present an effective approach to melanoma classification from dermoscopic images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. Shape features are then extracted from this border, while colour and texture features are obtained based on a division of the image into clinically significant regions. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and the use of our classifier ensemble to lead to statistically better recognition performance.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122930802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper mainly introduces the techniques required for a future system, called Magic Mirror. Imagine when you wake up in the morning and prepare for the coming day, the Magic Mirror will automatically recommend to you the most appropriate styles of hair, makeup, and dressing, according to the events and activities on your calendar, with which it is linked, so that you can present yourself on these occasions with elegant and suitable appearance. The work shall focus on the mathematical models for these tasks, particularly on how to model the relations between low-level human body features, middle-level facial/body attributes, and high-level recommendations. Being automatic and intelligent are the two main characteristics of the system, and this work shall show two prototype sub-systems related with the whole Magic Mirror system.
{"title":"Magic Mirror: An Intelligent Fashion Recommendation System","authors":"Si Liu, Luoqi Liu, Shuicheng Yan","doi":"10.1109/ACPR.2013.212","DOIUrl":"https://doi.org/10.1109/ACPR.2013.212","url":null,"abstract":"This paper mainly introduces the techniques required for a future system, called Magic Mirror. Imagine when you wake up in the morning and prepare for the coming day, the Magic Mirror will automatically recommend to you the most appropriate styles of hair, makeup, and dressing, according to the events and activities on your calendar, with which it is linked, so that you can present yourself on these occasions with elegant and suitable appearance. The work shall focus on the mathematical models for these tasks, particularly on how to model the relations between low-level human body features, middle-level facial/body attributes, and high-level recommendations. Being automatic and intelligent are the two main characteristics of the system, and this work shall show two prototype sub-systems related with the whole Magic Mirror system.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122289356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is applied to face recognition and is evaluated using the Extended Yale B and CMU PIE databases. Experimental results demonstrate that our method is more effective and robust than PCA, 2DPCA and L1-Norm based 2DPCA.
{"title":"Nuclear Norm Based 2DPCA","authors":"Fanlong Zhang, J. Qian, Jian Yang","doi":"10.1109/ACPR.2013.10","DOIUrl":"https://doi.org/10.1109/ACPR.2013.10","url":null,"abstract":"This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is applied to face recognition and is evaluated using the Extended Yale B and CMU PIE databases. Experimental results demonstrate that our method is more effective and robust than PCA, 2DPCA and L1-Norm based 2DPCA.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125464308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human age estimation based on facial images has many potential applications in practice. However, the current age estimation techniques are not matured. Most studies focus only on neutral faces, that is, expressionless faces. Several expressions such as happy expression, may help to improve the prediction accuracy. Recently, some works reported that expressions could badly impact on the accuracy. In this paper, we investigated the degree of facial expression impact on age prediction subjectively and objectively. It was revealed that expressions do not contribute for age prediction so much.
{"title":"How Do Facial Expressions Contribute to Age Prediction?","authors":"Yingmei Piao, Mineichi Kudo","doi":"10.1109/ACPR.2013.161","DOIUrl":"https://doi.org/10.1109/ACPR.2013.161","url":null,"abstract":"Human age estimation based on facial images has many potential applications in practice. However, the current age estimation techniques are not matured. Most studies focus only on neutral faces, that is, expressionless faces. Several expressions such as happy expression, may help to improve the prediction accuracy. Recently, some works reported that expressions could badly impact on the accuracy. In this paper, we investigated the degree of facial expression impact on age prediction subjectively and objectively. It was revealed that expressions do not contribute for age prediction so much.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121454415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tadashi Hyuga, H. Wada, Tomoyoshi Aizawa, Yoshihisa Ijiri, M. Kawade
In this demonstration, we will show our Optical Character Recognition(OCR) technique. Character deformation and touching problems often occur during high-speed printing process in the machine vision industry. As a result, it is difficult for OCR system to segment and recognize characters properly. To solve these problems, we propose a novel OCR technique which is robust against deformation and touching. It splits regions of characters simply and excessively, recognizes all segments and merged regions, and obtains optimal segments using graph theory.
{"title":"Deformed and Touched Characters Recognition","authors":"Tadashi Hyuga, H. Wada, Tomoyoshi Aizawa, Yoshihisa Ijiri, M. Kawade","doi":"10.1109/ACPR.2013.193","DOIUrl":"https://doi.org/10.1109/ACPR.2013.193","url":null,"abstract":"In this demonstration, we will show our Optical Character Recognition(OCR) technique. Character deformation and touching problems often occur during high-speed printing process in the machine vision industry. As a result, it is difficult for OCR system to segment and recognize characters properly. To solve these problems, we propose a novel OCR technique which is robust against deformation and touching. It splits regions of characters simply and excessively, recognizes all segments and merged regions, and obtains optimal segments using graph theory.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127876425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Yoshinaga, Atsushi Shimada, H. Nagahara, R. Taniguchi, Kouichiro Kajitani, Takeshi Naito
Many background models have been proposed to adapt to "illumination changes" and "dynamic changes" such as swaying motion of tree branches. However, the problem of background maintenance in complex environment, where foreground objects pass in front of stationary objects which cease moving, is still far from being completely solved. To address this problem, we propose a framework for multi-layered background modeling, in which we conserve the background models for stationary objects hierarchically in addition to the one for the initial background. To realize this framework, we also propose a spatio-temporal background model based on the similarity in the intensity changes among pixels. Experimental results on complex scenes, such as a bus stop and an intersection, show that our proposed method can adapt to both appearances and disappearances of stationary objects thanks to the multi-layered background modeling framework.
{"title":"Multi-layered Background Modeling for Complex Environment Surveillance","authors":"S. Yoshinaga, Atsushi Shimada, H. Nagahara, R. Taniguchi, Kouichiro Kajitani, Takeshi Naito","doi":"10.1109/ACPR.2013.83","DOIUrl":"https://doi.org/10.1109/ACPR.2013.83","url":null,"abstract":"Many background models have been proposed to adapt to \"illumination changes\" and \"dynamic changes\" such as swaying motion of tree branches. However, the problem of background maintenance in complex environment, where foreground objects pass in front of stationary objects which cease moving, is still far from being completely solved. To address this problem, we propose a framework for multi-layered background modeling, in which we conserve the background models for stationary objects hierarchically in addition to the one for the initial background. To realize this framework, we also propose a spatio-temporal background model based on the similarity in the intensity changes among pixels. Experimental results on complex scenes, such as a bus stop and an intersection, show that our proposed method can adapt to both appearances and disappearances of stationary objects thanks to the multi-layered background modeling framework.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127767480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gait recognition is a method of biometric person authentication from his/her unconscious walking manner. Unlike the other biometrics such as DNA, fingerprint, vein, and iris, the gait can be recognized even at a distance from a camera without subjects' cooperation, and hence it is expected to be applied to many fields: criminal investigation, forensic science, and surveillance. However, the absence of the subjects' cooperation may sometimes induces large intra-subject variations of the gait due to the changes of viewpoints, walking directions, speeds, clothes, and shoes. We therefore develop methods of robust gait recognition with (1) an appearance-based view transformation model, (2) a kinematics-based speed transformation model. Moreover, CCTV footages are often stored as low frame-rate videos due to limitation of communication bandwidth and storage size, which makes it much more difficult to observe a continuous gait motion and hence significantly degrades the gait recognition performance. We therefore solve this problem with (3) a technique of periodic temporal super resolution from a low frame-rate video. We show the efficiency of the proposed methods with our constructed gait databases.
{"title":"Towards Robust Gait Recognition","authors":"Yasushi Makihara","doi":"10.1109/ACPR.2013.211","DOIUrl":"https://doi.org/10.1109/ACPR.2013.211","url":null,"abstract":"Gait recognition is a method of biometric person authentication from his/her unconscious walking manner. Unlike the other biometrics such as DNA, fingerprint, vein, and iris, the gait can be recognized even at a distance from a camera without subjects' cooperation, and hence it is expected to be applied to many fields: criminal investigation, forensic science, and surveillance. However, the absence of the subjects' cooperation may sometimes induces large intra-subject variations of the gait due to the changes of viewpoints, walking directions, speeds, clothes, and shoes. We therefore develop methods of robust gait recognition with (1) an appearance-based view transformation model, (2) a kinematics-based speed transformation model. Moreover, CCTV footages are often stored as low frame-rate videos due to limitation of communication bandwidth and storage size, which makes it much more difficult to observe a continuous gait motion and hence significantly degrades the gait recognition performance. We therefore solve this problem with (3) a technique of periodic temporal super resolution from a low frame-rate video. We show the efficiency of the proposed methods with our constructed gait databases.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129056148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detecting compact overlapping communities in large networks is an important pattern recognition problem with applications in many domains. Most community detection algorithms trade-off between community sizes, their compactness and the scalability of finding communities. Clique Percolation Method (CPM) and Local Fitness Maximization (LFM) are two prominent and commonly used overlapping community detection methods that scale with large networks. However, significant number of communities found by them are large, noisy, and loose. In this paper, we propose a general algorithm that takes such large and loose communities generated by any method and refines them into compact communities in a systematic fashion. We define a new measure of community-ness based on eigenvector centrality, identify loose communities using this measure and propose an algorithm for partitioning such loose communities into compact communities. We refine the communities found by CPM and LFM using our method and show their effectiveness compared to the original communities in a recommendation engine task.
{"title":"Compacting Large and Loose Communities","authors":"V. Chandrashekar, Shailesh Kumar, C. V. Jawahar","doi":"10.1109/ACPR.2013.137","DOIUrl":"https://doi.org/10.1109/ACPR.2013.137","url":null,"abstract":"Detecting compact overlapping communities in large networks is an important pattern recognition problem with applications in many domains. Most community detection algorithms trade-off between community sizes, their compactness and the scalability of finding communities. Clique Percolation Method (CPM) and Local Fitness Maximization (LFM) are two prominent and commonly used overlapping community detection methods that scale with large networks. However, significant number of communities found by them are large, noisy, and loose. In this paper, we propose a general algorithm that takes such large and loose communities generated by any method and refines them into compact communities in a systematic fashion. We define a new measure of community-ness based on eigenvector centrality, identify loose communities using this measure and propose an algorithm for partitioning such loose communities into compact communities. We refine the communities found by CPM and LFM using our method and show their effectiveness compared to the original communities in a recommendation engine task.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129125574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a real-time object recognition method for a smart phone, which consists of light-weight local features, Fisher Vector and linear SVM. As light local descriptors, we adopt a HOG Patch descriptor and a Color Patch descriptor, and sample them from an image densely. Then we encode them with Fisher Vector representation, which can save the number of visual words greatly. As a classifier, we use a liner SVM the computational cost of which is very low. In the experiments, we have achieved the 79.2% classification rate for the top 5 category candidates for a 100-category food dataset. It outperformed the results using a conventional bag-of-features representation with a chi-square-RBF-kernel-based SVM. Moreover, the processing time of food recognition takes only 0.065 seconds, which is four times as faster as the existing work.
{"title":"Rapid Mobile Object Recognition Using Fisher Vector","authors":"Yoshiyuki Kawano, Keiji Yanai","doi":"10.1109/ACPR.2013.39","DOIUrl":"https://doi.org/10.1109/ACPR.2013.39","url":null,"abstract":"We propose a real-time object recognition method for a smart phone, which consists of light-weight local features, Fisher Vector and linear SVM. As light local descriptors, we adopt a HOG Patch descriptor and a Color Patch descriptor, and sample them from an image densely. Then we encode them with Fisher Vector representation, which can save the number of visual words greatly. As a classifier, we use a liner SVM the computational cost of which is very low. In the experiments, we have achieved the 79.2% classification rate for the top 5 category candidates for a 100-category food dataset. It outperformed the results using a conventional bag-of-features representation with a chi-square-RBF-kernel-based SVM. Moreover, the processing time of food recognition takes only 0.065 seconds, which is four times as faster as the existing work.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heterogeneous Face Recognition (HFR) refers to matching probe face images to a gallery of face images taken from alternate imaging modality, for example matching near infrared (NIR) face images to photographs. Matching heterogeneous face images has important practical applications such as surveillance and forensics, which is yet a challenging problem in face recognition community due to the large within-class discrepancy incurred from modality differences. In this paper, a novel feature descriptor is proposed in which the features of both gallery and probe face images are extracted with an adaptive feature descriptor which can maximize the correlation of the encoded face images between the modalities, so as to reduce the within-class variations at the feature extraction stage. The effectiveness of the proposed approach is demonstrated on the scenario of matching NIR face images to photographs based on a very large dataset consists of 2800 different persons.
{"title":"A Maximum Correlation Feature Descriptor for Heterogeneous Face Recognition","authors":"Dihong Gong, J. Zheng","doi":"10.1109/ACPR.2013.12","DOIUrl":"https://doi.org/10.1109/ACPR.2013.12","url":null,"abstract":"Heterogeneous Face Recognition (HFR) refers to matching probe face images to a gallery of face images taken from alternate imaging modality, for example matching near infrared (NIR) face images to photographs. Matching heterogeneous face images has important practical applications such as surveillance and forensics, which is yet a challenging problem in face recognition community due to the large within-class discrepancy incurred from modality differences. In this paper, a novel feature descriptor is proposed in which the features of both gallery and probe face images are extracted with an adaptive feature descriptor which can maximize the correlation of the encoded face images between the modalities, so as to reduce the within-class variations at the feature extraction stage. The effectiveness of the proposed approach is demonstrated on the scenario of matching NIR face images to photographs based on a very large dataset consists of 2800 different persons.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133790830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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