Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344137
Shuli Han, Bo Yuan, Wenhuang Liu
Rare class problems exist extensively in real-world applications across a wide range of domains. The extreme scarcity of the target class challenges traditional machine learning algorithms focusing on the overall classification accuracy. As a result, purposefully designed techniques are required for effectively solving the rare class mining problem. This paper presents a systematic review of the major representative approaches to rare class mining and related topics and gives a summary of the important research directions.
{"title":"Rare Class Mining: Progress and Prospect","authors":"Shuli Han, Bo Yuan, Wenhuang Liu","doi":"10.1109/CCPR.2009.5344137","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344137","url":null,"abstract":"Rare class problems exist extensively in real-world applications across a wide range of domains. The extreme scarcity of the target class challenges traditional machine learning algorithms focusing on the overall classification accuracy. As a result, purposefully designed techniques are required for effectively solving the rare class mining problem. This paper presents a systematic review of the major representative approaches to rare class mining and related topics and gives a summary of the important research directions.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132511212","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5343973
Jun Ye, Lin-Lin Huang, X. Hao
The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial neural network (PNN) is employed to make classification. The PNN is trained with large quantities of samples collected using a bootstrap strategy. In addition, post-processing procedure including verification and integration is performed to refine the detected results. The effectiveness of the proposed method is demonstrated by experimental results.
{"title":"Neural Network Based Text Detection in Videos Using Local Binary Patterns","authors":"Jun Ye, Lin-Lin Huang, X. Hao","doi":"10.1109/CCPR.2009.5343973","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5343973","url":null,"abstract":"The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial neural network (PNN) is employed to make classification. The PNN is trained with large quantities of samples collected using a bootstrap strategy. In addition, post-processing procedure including verification and integration is performed to refine the detected results. The effectiveness of the proposed method is demonstrated by experimental results.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117276801","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}
As one of important information component in multimedia, audio enriches information perception and acquisition. Analyses and extractions of audio features are the base of audio classification. It's important to extract audio features effectively for content-based audio retrieval. In this paper, based on the theory of rough set, audio features are reduced and a lower-dimension feature set can be obtained with more effective. Then the feature set is applied in the general model for audio classification. Experiments show that this method is effective.
{"title":"A Method Based on General Model and Rough Set for Audio Classification","authors":"Xin He, Ying-Chun Shi, Fuming Peng, Xianzhong Zhou","doi":"10.1109/CCPR.2009.5344044","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344044","url":null,"abstract":"As one of important information component in multimedia, audio enriches information perception and acquisition. Analyses and extractions of audio features are the base of audio classification. It's important to extract audio features effectively for content-based audio retrieval. In this paper, based on the theory of rough set, audio features are reduced and a lower-dimension feature set can be obtained with more effective. Then the feature set is applied in the general model for audio classification. Experiments show that this method is effective.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100454","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5343964
Hui Xu, Y. Qu, Yan Zhang, Feng Zhao
A critical step in fingerprint recognition is to skeletonize the fingerprint image for minutiae extraction, which is recognized as "thinning" in image processing. The speed and reliability of the thinning process are important for the whole fingerprint identification system. In this paper, to accelerate the thinning process, a fast hardware thinning algorithm is implemented on the Xilinx Virtex II Pro developing system with a highly- paralleled architecture. Appealing experimental result is presented and the advantage of hardware thinning is also explored.
指纹识别的一个关键步骤是对指纹图像进行骨架化以提取细节，这在图像处理中被称为“细化”。细化过程的速度和可靠性对整个指纹识别系统至关重要。为了加速细化过程，本文在高并行架构的Xilinx Virtex II Pro开发系统上实现了一种快速硬件细化算法。给出了令人满意的实验结果，并探讨了硬件细化的优点。
{"title":"FPGA Based Parallel Thinning for Binary Fingerprint Image","authors":"Hui Xu, Y. Qu, Yan Zhang, Feng Zhao","doi":"10.1109/CCPR.2009.5343964","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5343964","url":null,"abstract":"A critical step in fingerprint recognition is to skeletonize the fingerprint image for minutiae extraction, which is recognized as \"thinning\" in image processing. The speed and reliability of the thinning process are important for the whole fingerprint identification system. In this paper, to accelerate the thinning process, a fast hardware thinning algorithm is implemented on the Xilinx Virtex II Pro developing system with a highly- paralleled architecture. Appealing experimental result is presented and the advantage of hardware thinning is also explored.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121247176","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344071
Wenjia Yang, L. Dou, Juan Zhang
To solve the problem of moving object segmentation in video sequence, a new video moving object segmentation algorithm was proposed based on Kirsch edge operator. The detected edge is mainly analyzed for segmentation and motion vector field is taken as assistant information. Firstly, the motion vectors are processed by accumulation and median filer. Secondly, templates of Kirsch operators are decomposed into difference templates and common templates to find the edge position; then, the edge information and the motion vectors are fused to get moving object by adaptive state labeling. The experimental results show the proposed algorithm has a better veracity of segmentation.
{"title":"Video Moving Object Segmentation Algorithm Based on an Improved Kirsch Edge Operator","authors":"Wenjia Yang, L. Dou, Juan Zhang","doi":"10.1109/CCPR.2009.5344071","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344071","url":null,"abstract":"To solve the problem of moving object segmentation in video sequence, a new video moving object segmentation algorithm was proposed based on Kirsch edge operator. The detected edge is mainly analyzed for segmentation and motion vector field is taken as assistant information. Firstly, the motion vectors are processed by accumulation and median filer. Secondly, templates of Kirsch operators are decomposed into difference templates and common templates to find the edge position; then, the edge information and the motion vectors are fused to get moving object by adaptive state labeling. The experimental results show the proposed algorithm has a better veracity of segmentation.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121313098","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5343954
Longbiao Wang, Yoshiki Kishi, A. Kai
Channel distortion for a distant environment may drastically degrade the performance of speaker recognition because the training and test conditions differ significantly. In this paper, we propose robust distant speaker recognition that is based on the automatic selection of reverberant environments using Gaussian mixture models. Three methods involving (I) optimum channel determination, (II) joint optimum speaker and channel determination, or (III) optimum channel determination at the utterance level are proposed. Real-world speech data and simulated reverberant speech data are used to evaluate our proposed methods. The third proposed method achieves a relative error reduction of 69.6% over (baseline) speaker recognition using a reverberant environment-independent method, and it has performance equivalent to that of a
{"title":"Distant Speaker Recognition Based on the Automatic Selection of Reverberant Environments Using GMMs","authors":"Longbiao Wang, Yoshiki Kishi, A. Kai","doi":"10.1109/CCPR.2009.5343954","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5343954","url":null,"abstract":"Channel distortion for a distant environment may drastically degrade the performance of speaker recognition because the training and test conditions differ significantly. In this paper, we propose robust distant speaker recognition that is based on the automatic selection of reverberant environments using Gaussian mixture models. Three methods involving (I) optimum channel determination, (II) joint optimum speaker and channel determination, or (III) optimum channel determination at the utterance level are proposed. Real-world speech data and simulated reverberant speech data are used to evaluate our proposed methods. The third proposed method achieves a relative error reduction of 69.6% over (baseline) speaker recognition using a reverberant environment-independent method, and it has performance equivalent to that of a","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837875","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344079
Ziqiang Wang, Xia Sun
To efficiently deal with the curse of dimensionality in the content-based image retrieval(CBIR) system, a novel image retrieval algorithm is proposed by combination of local discriminant embedding(LDE) and least square SVM(LS-SVM) in this paper. LDE aims to achieve good discriminating performance by integrating the local geometrical structure and class relations between image data. LS-SVM classifier is used to classify the retrieved image into relevant or irrelevant image based on extracted low-level visual features. Experimental results on real-world image collection demonstrate that the proposed algorithm performs much better than other related image retrieval algorithms.
{"title":"Image Retrieval Using Discriminant Embedding and LS-SVM","authors":"Ziqiang Wang, Xia Sun","doi":"10.1109/CCPR.2009.5344079","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344079","url":null,"abstract":"To efficiently deal with the curse of dimensionality in the content-based image retrieval(CBIR) system, a novel image retrieval algorithm is proposed by combination of local discriminant embedding(LDE) and least square SVM(LS-SVM) in this paper. LDE aims to achieve good discriminating performance by integrating the local geometrical structure and class relations between image data. LS-SVM classifier is used to classify the retrieved image into relevant or irrelevant image based on extracted low-level visual features. Experimental results on real-world image collection demonstrate that the proposed algorithm performs much better than other related image retrieval algorithms.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123942699","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344150
Lihua Guo, Lianwen Jin
Multi-instance and Multi-Label (MIML) machine learning has been employed in the generic object classification for it's gracefully performance in solving the ambiguity of image. The whole image is regarded as a multi-instance bag. The image is separated into four parts, whose edge's histograms are calculated. These input vectors can be combined a multi-instance ones for adapting the MIML learning. The experimental results show that the average precise ratio of our method is higher 3% than one of the traditional Support Vector Machine method.
{"title":"The Generic Object Classification Based on MIML Machine Learning","authors":"Lihua Guo, Lianwen Jin","doi":"10.1109/CCPR.2009.5344150","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344150","url":null,"abstract":"Multi-instance and Multi-Label (MIML) machine learning has been employed in the generic object classification for it's gracefully performance in solving the ambiguity of image. The whole image is regarded as a multi-instance bag. The image is separated into four parts, whose edge's histograms are calculated. These input vectors can be combined a multi-instance ones for adapting the MIML learning. The experimental results show that the average precise ratio of our method is higher 3% than one of the traditional Support Vector Machine method.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124923506","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}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344074
Yu Song, Qingling Li, F. Sun
Shannon entropy is effective uncertainty measurement criterion for stochastic system. In this paper, adaptive fusion particle filter is proposed for visual tracking by introduced Shannon entropy in particle filter framework. Firstly, the particle filter, which is considered as the process of particles assimilating negative entropy to reduce uncertainty, is surveyed from viewpoint of information theory. Secondly, maximum negative entropy criterion is proposed to select tracking feature form features pool online. At last, color histogram and edge orientation histogram features are utilized in experiments, tracking results show that the proposed algorithm is a robust and accuracy tracking algorithm.
{"title":"Shannon Entropy-Based Adaptive Fusion Particle Filter for Visual Tracking","authors":"Yu Song, Qingling Li, F. Sun","doi":"10.1109/CCPR.2009.5344074","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344074","url":null,"abstract":"Shannon entropy is effective uncertainty measurement criterion for stochastic system. In this paper, adaptive fusion particle filter is proposed for visual tracking by introduced Shannon entropy in particle filter framework. Firstly, the particle filter, which is considered as the process of particles assimilating negative entropy to reduce uncertainty, is surveyed from viewpoint of information theory. Secondly, maximum negative entropy criterion is proposed to select tracking feature form features pool online. At last, color histogram and edge orientation histogram features are utilized in experiments, tracking results show that the proposed algorithm is a robust and accuracy tracking algorithm.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121320504","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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