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2010 20th International Conference on Pattern Recognition最新文献

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Action Recognition Using Three-Way Cross-Correlations Feature of Local Moton Attributes 基于局部运动属性三向互关特征的动作识别
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.428
Tetsu Matsukawa, Takio Kurita
{"title":"Action Recognition Using Three-Way Cross-Correlations Feature of Local Moton Attributes","authors":"Tetsu Matsukawa, Takio Kurita","doi":"10.1109/ICPR.2010.428","DOIUrl":"https://doi.org/10.1109/ICPR.2010.428","url":null,"abstract":"This paper proposes a spatio-temporal feature using three-way cross-correlations of local motion attributes for action recognition. Recently, the cubic higher-order local auto-correlations (CHLAC) feature has been shown high classification performances for action recognition. In previous researches, CHLAC feature was applied to binary motion image sequences that indicates moving or static points. However, each binary motion image lost informations about the type of motion such as timing of change or motion direction. Therefore, we can improve the classification accuracy further by extending CHLAC to multivalued motion image sequences that considered several types of local motion attributes. The proposed method is also viewed as an extension of popular bag-of-features approach. Experimental results using two datasets shows proposed method outperformed CHLAC features and bag-of-features approach.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122670450","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}
引用次数: 9
A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides 病理切片最佳分类的多实例学习方法
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.669
M. Dundar, S. Badve, V. Raykar, R. Jain, Olcay Sertel, M. Gürcan
{"title":"A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides","authors":"M. Dundar, S. Badve, V. Raykar, R. Jain, Olcay Sertel, M. Gürcan","doi":"10.1109/ICPR.2010.669","DOIUrl":"https://doi.org/10.1109/ICPR.2010.669","url":null,"abstract":"Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has to rule out cancer for each and every ROI available. Our research is motivated toward computer-assisted classification of digitized slides. The objective in this study is to develop a classifier to optimize classification accuracy at the slide level. Traditional supervised training techniques which are trained to optimize classifier performance at the ROI level yield suboptimal performance in this problem. We propose a multiple instance learning approach based on the implementation of the large margin principle with different loss functions defined for positive and negative samples. We consider the classification of intraductal breast lesions as a case study, and perform experimental studies comparing our approach against the state-of-the-art.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674951","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}
引用次数: 41
Cluster-Pairwise Discriminant Analysis 聚类两两判别分析
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.146
Yasushi Makihara, Y. Yagi
{"title":"Cluster-Pairwise Discriminant Analysis","authors":"Yasushi Makihara, Y. Yagi","doi":"10.1109/ICPR.2010.146","DOIUrl":"https://doi.org/10.1109/ICPR.2010.146","url":null,"abstract":"Pattern recognition problems often suffer from the larger intra-class variation due to situation variations such as pose, walking speed, and clothing variations in gait recognition. This paper describes a method of discriminant subspace analysis focused on situation cluster pair. In training phase, both a situation cluster discriminant subspace and class discriminant subspaces for the situation cluster pair by using training samples of non recognition-target classes. In testing phase, given a matching pair of patterns of recognition-target classes, posterior of situation cluster pairs is estimated at first, and then the distance is calculated in the corresponding cluster-pairwise class discriminant subspace. The experiments both with simulation data and real data show the effectiveness of the proposed method.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703147","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}
引用次数: 3
Theoretical Analysis of a Performance Measure for Imbalanced Data 不平衡数据性能度量的理论分析
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.156
V. García, R. A. Mollineda, J. S. Sánchez
{"title":"Theoretical Analysis of a Performance Measure for Imbalanced Data","authors":"V. García, R. A. Mollineda, J. S. Sánchez","doi":"10.1109/ICPR.2010.156","DOIUrl":"https://doi.org/10.1109/ICPR.2010.156","url":null,"abstract":"This paper analyzes a generalization of a new metric to evaluate the classification performance in imbalanced domains, combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual accuracy is. A theoretical analysis shows the merits of this metric when compared to other well-known measures.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122540640","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}
引用次数: 51
Rethinking Algorithm Design and Development in Speech Processing 语音处理中算法设计与开发的再思考
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.1087
Thilo Stadelmann, Yinghui Wang, Matthew Smith, R. Ewerth, Bernd Freisleben
{"title":"Rethinking Algorithm Design and Development in Speech Processing","authors":"Thilo Stadelmann, Yinghui Wang, Matthew Smith, R. Ewerth, Bernd Freisleben","doi":"10.1109/ICPR.2010.1087","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1087","url":null,"abstract":"Speech processing is typically based on a set of complex algorithms requiring many parameters to be specified. When parts of the speech processing chain do not behave as expected, trial and error is often the only way to investigate the reasons. In this paper, we present a research methodology to analyze unexpected algorithmic behavior by making (intermediate) results of the speech processing chain perceivable and intuitively comprehensible by humans. The workflow of the process is explicated using a real-world example leading to considerable improvements in speaker clustering. The described methodology is supported by a software toolbox available for download.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122663563","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}
引用次数: 4
Video Retrieval Based on Tracked Features Quantization 基于跟踪特征量化的视频检索
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.794
Hiroaki Kubo, Julien Pilet, H. Saito, S. Satoh
{"title":"Video Retrieval Based on Tracked Features Quantization","authors":"Hiroaki Kubo, Julien Pilet, H. Saito, S. Satoh","doi":"10.1109/ICPR.2010.794","DOIUrl":"https://doi.org/10.1109/ICPR.2010.794","url":null,"abstract":"In this paper, we present an image retrieval method based on feature tracking. Feature tracks are summarized into a compact discreet value and used for video indexing purpose. As opposed to existing space-time features, we do not make any assumption on the motion visible on the indexed videos. As a result, given an example query, our system is able to retrieve related videos from a large database. We evaluated our system with the copy detection benchmark MUSCLE-VCD-2007. We also ran retrieval experiment on hours of TV broadcast.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122841916","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}
引用次数: 0
A Self-Training Learning Document Binarization Framework 一个自训练学习文档二值化框架
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.780
Bolan Su, Shijian Lu, C. Tan
{"title":"A Self-Training Learning Document Binarization Framework","authors":"Bolan Su, Shijian Lu, C. Tan","doi":"10.1109/ICPR.2010.780","DOIUrl":"https://doi.org/10.1109/ICPR.2010.780","url":null,"abstract":"Document Image Binarization techniques have been studied for many years, and many practical binarization techniques have been developed and applied successfully on commercial document analysis systems. However, the current state-of-the-art methods, fail to produce good binarization results for many badly degraded document images. In this paper, we propose a self-training learning framework for document image binarization. Based on reported binarization methods, the proposed framework first divides document image pixels into three categories, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then trained by learning from the document image pixels in the foreground and background categories. Finally, the uncertain pixels are classified using the learned pixel classifier. Extensive experiments have been conducted over the dataset that is used in the recent Document Image Binarization Contest(DIBCO) 2009. Experimental results show that our proposed framework significantly improves the performance of reported document image binarization methods.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122907105","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}
引用次数: 34
Reverse Indexing for Reading Graffiti Tags 反向索引阅读涂鸦标签
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.740
Christian Thurau, C. Bauckhage
{"title":"Reverse Indexing for Reading Graffiti Tags","authors":"Christian Thurau, C. Bauckhage","doi":"10.1109/ICPR.2010.740","DOIUrl":"https://doi.org/10.1109/ICPR.2010.740","url":null,"abstract":"In this paper, we consider the problem of automatically reading graffiti tags. As a preparatory step, we create a large set of synthetic graffiti-like characters, generated from publicly available true type fonts. For each character in the database, we extract a number of scale independent local binary descriptors. Then, using binary non negative matrix factorization, a sufficient number of basis functions are learned. Basis function coefficients of novel images can then be directly used for hashing characters from the database of prototypes. Finally, graffiti tags are recognized by means of a localized, spatial voting scheme.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122980246","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}
引用次数: 0
Exploiting System Knowledge to Improve ECOC Reject Rules 利用系统知识改进ECOC拒收规则
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.1055
P. Simeone, C. Marrocco, F. Tortorella
{"title":"Exploiting System Knowledge to Improve ECOC Reject Rules","authors":"P. Simeone, C. Marrocco, F. Tortorella","doi":"10.1109/ICPR.2010.1055","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1055","url":null,"abstract":"Error Correcting Output Coding is a common technique for multiple class classification tasks which decomposes the original problem in several two-class problems solved through dichotomizers. Such classification system can be improved with a reject option which can be defined according to the level of information available from the dichotomizers. This paper analyzes how this knowledge is useful when applying such reject rules. The nature of the outputs, the kind of the employed classifiers and the knowledge of their loss function are influential details for the improvement of the general performance of the system. Experimental results on popular benchmark data sets are reported to show the behavior of the different schemes.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"138 1-3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114047172","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}
引用次数: 4
Learning Naive Bayes Classifiers for Music Classification and Retrieval 学习朴素贝叶斯分类器用于音乐分类和检索
2010 20th International Conference on Pattern Recognition Pub Date : 2010-08-23 DOI: 10.1109/ICPR.2010.1121
Zhouyu Fu, Guojun Lu, K. Ting, Dengsheng Zhang
{"title":"Learning Naive Bayes Classifiers for Music Classification and Retrieval","authors":"Zhouyu Fu, Guojun Lu, K. Ting, Dengsheng Zhang","doi":"10.1109/ICPR.2010.1121","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1121","url":null,"abstract":"In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor and support vector machine classifiers. Experimental results have demonstrated superior performance achieved by the proposed naive Bayes classifiers for both music classification and retrieval as compared to the alternative methods.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184179","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}
引用次数: 23
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