Pub Date : 2011-11-01DOI: 10.1109/ACPR.2011.6166581
Fei Yuan, Gui-Song Xia, H. Sahbi, V. Prinet
We present a novel feature, named Spatio-Temporal Interest Points Chain (STIPC), for activity representation and recognition. This new feature consists of a set of trackable spatio-temporal interest points, which correspond to a series of discontinuous motion among a long-term motion of an object or its part. By this chain feature, we can not only capture the discriminative motion information which space-time interest point-like feature try to pursue, but also build the connection between them. Specifically, we first extract the point trajectories from the image sequences, then partition the points on each trajectory into two kinds of different yet close related points: discontinuous motion points and continuous motion points. We extract local space-time features around discontinuous motion points and use a chain model to represent them. Furthermore, we introduce a chain descriptor to encode the temporal relationships between these interdependent local space-time features. The experimental results on challenging datasets show that our STIPC features improves local space-time features and achieve state-of-the-art results.
{"title":"Spatio-Temporal Interest Points Chain (STIPC) for activity recognition","authors":"Fei Yuan, Gui-Song Xia, H. Sahbi, V. Prinet","doi":"10.1109/ACPR.2011.6166581","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166581","url":null,"abstract":"We present a novel feature, named Spatio-Temporal Interest Points Chain (STIPC), for activity representation and recognition. This new feature consists of a set of trackable spatio-temporal interest points, which correspond to a series of discontinuous motion among a long-term motion of an object or its part. By this chain feature, we can not only capture the discriminative motion information which space-time interest point-like feature try to pursue, but also build the connection between them. Specifically, we first extract the point trajectories from the image sequences, then partition the points on each trajectory into two kinds of different yet close related points: discontinuous motion points and continuous motion points. We extract local space-time features around discontinuous motion points and use a chain model to represent them. Furthermore, we introduce a chain descriptor to encode the temporal relationships between these interdependent local space-time features. The experimental results on challenging datasets show that our STIPC features improves local space-time features and achieve state-of-the-art results.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123790948","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166549
Wei Jiang, Wenju Liu, Pengfei Hu
The smoothness of spectral envelope is a commonly known attribute of clean speech. In this study, this principle is modeled through oscillation degree of each time-frequency (T-F) unit, and then incorporated into a computational auditory scene analysis (CASA) system for monaural voiced speech separation. Specifically, oscillation degrees of autocorrelation function (ODACF) and of envelope autocorrelation function (ODEACF) are extracted for each T-F unit, which are then utilized in T-F unit labeling. Experiment results indicate that target units and interference units are distinguished more effectively by incorporating the spectral smoothness principle than by using the harmonic principle alone, and obvious segregation improvements are obtained.
{"title":"Modeling spectral smoothness principle for monaural voiced speech separation","authors":"Wei Jiang, Wenju Liu, Pengfei Hu","doi":"10.1109/ACPR.2011.6166549","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166549","url":null,"abstract":"The smoothness of spectral envelope is a commonly known attribute of clean speech. In this study, this principle is modeled through oscillation degree of each time-frequency (T-F) unit, and then incorporated into a computational auditory scene analysis (CASA) system for monaural voiced speech separation. Specifically, oscillation degrees of autocorrelation function (ODACF) and of envelope autocorrelation function (ODEACF) are extracted for each T-F unit, which are then utilized in T-F unit labeling. Experiment results indicate that target units and interference units are distinguished more effectively by incorporating the spectral smoothness principle than by using the harmonic principle alone, and obvious segregation improvements are obtained.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122940358","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166647
Zhihui Lai, Qingcai Chen, Zhong Jin
The techniques of linear dimensionality reduction have been attracted widely attention in the fields of computer vision and pattern recognition. In this paper, we propose a novel framework called Sparse Bilinear Preserving Projections (SBPP) for image feature extraction. We generalized the image-based bilinear preserving projections into sparse case for feature extraction. Different from the popular bilinear linear projection techniques, the projections of SBPP are sparse, i.e. most elements in the projections are zeros. In the proposed framework, we use the local neighborhood graph to model the manifold structure of the data set at first, and then spectral analysis and L1-norm regression by using the Elastic Net are combined together to iteratively learn the sparse bilinear projections, which optimal preserve the local geometric structure of the image manifold. Experiments on some databases show that SBPP is competitive to some state-of-the-art techniques.
{"title":"Sparse bilinear preserving projections","authors":"Zhihui Lai, Qingcai Chen, Zhong Jin","doi":"10.1109/ACPR.2011.6166647","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166647","url":null,"abstract":"The techniques of linear dimensionality reduction have been attracted widely attention in the fields of computer vision and pattern recognition. In this paper, we propose a novel framework called Sparse Bilinear Preserving Projections (SBPP) for image feature extraction. We generalized the image-based bilinear preserving projections into sparse case for feature extraction. Different from the popular bilinear linear projection techniques, the projections of SBPP are sparse, i.e. most elements in the projections are zeros. In the proposed framework, we use the local neighborhood graph to model the manifold structure of the data set at first, and then spectral analysis and L1-norm regression by using the Elastic Net are combined together to iteratively learn the sparse bilinear projections, which optimal preserve the local geometric structure of the image manifold. Experiments on some databases show that SBPP is competitive to some state-of-the-art techniques.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127670946","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166706
Sujing Wang, Chengcheng Jia, Huiling Chen, Bo Wu, Chunguang Zhou
Face recognition plays a important role in computer vision. Recent researches show that high dimensional face images lie on or close to a low dimensional manifold. LPP is a widely used manifold reduced dimensionality technique. But it suffers two problem: (1) Small Sample Size problem; (2)the performance is sensitive to the neighborhood size k. In order to address the problems, this paper proposed a Matrix Exponential LPP. To void the singular matrix, the proposed algorithm introduced the matrix exponential to obtain more valuable information for LPP. The experiments were conducted on two face database, Yale and Georgia Tech. And the results proved the performances of the proposed algorithm was better than that of LPP.
{"title":"Matrix Exponential LPP for face recognition","authors":"Sujing Wang, Chengcheng Jia, Huiling Chen, Bo Wu, Chunguang Zhou","doi":"10.1109/ACPR.2011.6166706","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166706","url":null,"abstract":"Face recognition plays a important role in computer vision. Recent researches show that high dimensional face images lie on or close to a low dimensional manifold. LPP is a widely used manifold reduced dimensionality technique. But it suffers two problem: (1) Small Sample Size problem; (2)the performance is sensitive to the neighborhood size k. In order to address the problems, this paper proposed a Matrix Exponential LPP. To void the singular matrix, the proposed algorithm introduced the matrix exponential to obtain more valuable information for LPP. The experiments were conducted on two face database, Yale and Georgia Tech. And the results proved the performances of the proposed algorithm was better than that of LPP.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133535962","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166587
Tongtong Chen, Bin Dai, Daxue Liu, Bo Zhang, Qixu Liu
Obtaining a comprehensive model of large and complex ground typically is crucial for autonomous driving both in urban and countryside environments. This paper presents an improved ground segmentation method for 3D LIDAR point clouds. Our approach builds on a polar grid map, which is divided into some sectors, then 1D Gaussian process (GP) regression model and Incremental Sample Consensus (INSAC) algorithm is used to extract ground for every sector. Experiments are carried out at the autonomous vehicle in different outdoor scenes, and results are compared to those of the existing method. We show that our method can get more promising performance.
{"title":"3D LIDAR-based ground segmentation","authors":"Tongtong Chen, Bin Dai, Daxue Liu, Bo Zhang, Qixu Liu","doi":"10.1109/ACPR.2011.6166587","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166587","url":null,"abstract":"Obtaining a comprehensive model of large and complex ground typically is crucial for autonomous driving both in urban and countryside environments. This paper presents an improved ground segmentation method for 3D LIDAR point clouds. Our approach builds on a polar grid map, which is divided into some sectors, then 1D Gaussian process (GP) regression model and Incremental Sample Consensus (INSAC) algorithm is used to extract ground for every sector. Experiments are carried out at the autonomous vehicle in different outdoor scenes, and results are compared to those of the existing method. We show that our method can get more promising performance.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115022478","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166656
Zhong Zhang, Baihua Xiao, Chunheng Wang, Wen Zhou, Shuang Liu
Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem. In our method, the contextual constraints are explicitly added to the optimization objective function, which indicate the similarity relationship among neighboring pixels. In this way, the obtained de-mixing matrix can produce the complete foreground compared with the previous ICA model. In addition, our method performs robust to the indoor illumination changes and features a high processing speed. Two sets of image sequences involving room lights switching on/of and door opening/closing are tested. The experimental results clearly demonstrate an improvement over the basic ICA model and the image difference method.
{"title":"Contextual Constrained Independent Component Analysis based foreground detection for indoor surveillance","authors":"Zhong Zhang, Baihua Xiao, Chunheng Wang, Wen Zhou, Shuang Liu","doi":"10.1109/ACPR.2011.6166656","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166656","url":null,"abstract":"Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem. In our method, the contextual constraints are explicitly added to the optimization objective function, which indicate the similarity relationship among neighboring pixels. In this way, the obtained de-mixing matrix can produce the complete foreground compared with the previous ICA model. In addition, our method performs robust to the indoor illumination changes and features a high processing speed. Two sets of image sequences involving room lights switching on/of and door opening/closing are tested. The experimental results clearly demonstrate an improvement over the basic ICA model and the image difference method.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114332398","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166658
Xiao-Hua Liu, Cheng-Lin Liu
The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation. Given the number of mixture components (model order), the parameters of GMM can be estimated by the EM algorithm. The model order selection, however, remains an open problem. For classification purpose, we propose a discriminative model selection method to optimize the orders of all classes. Based on the GMMs initialized in some way, the orders of all classes are adjusted heuristically to improve the cross-validated classification accuracy. The model orders selected in this discriminative way are expected to give higher generalized accuracy than classwise model selection. Our experimental results on some UCI datasets demonstrate the superior classification performance of the proposed method.
{"title":"Discriminative model selection for Gaussian mixture models for classification","authors":"Xiao-Hua Liu, Cheng-Lin Liu","doi":"10.1109/ACPR.2011.6166658","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166658","url":null,"abstract":"The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation. Given the number of mixture components (model order), the parameters of GMM can be estimated by the EM algorithm. The model order selection, however, remains an open problem. For classification purpose, we propose a discriminative model selection method to optimize the orders of all classes. Based on the GMMs initialized in some way, the orders of all classes are adjusted heuristically to improve the cross-validated classification accuracy. The model orders selected in this discriminative way are expected to give higher generalized accuracy than classwise model selection. Our experimental results on some UCI datasets demonstrate the superior classification performance of the proposed method.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124703264","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166672
A. Hamad, N. Tsumura
This paper proposes a simple and a robust method to detect and extract the silhouettes from a video sequence of a static camera based on background subtraction technique. The proposed method analyse the pixel history as a time series observations. A robust technique to detect motion based on kernel density estimation is presented. Two consecutive stages of the k-means clustering algorithm are utilized to identify the most reliable background regions and decrease false positives. Pixel and object based updating mechanism is presented to cope with challenges like gradual and sudden illumination changes, ghost appearance, and non-stationary background objects. Experimental results show the efficiency and the robustness of the proposed method to detect and extract silhouettes for outdoor and indoor environments.
{"title":"Silhouette extraction based on time-series statistical modeling and k-means clustering","authors":"A. Hamad, N. Tsumura","doi":"10.1109/ACPR.2011.6166672","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166672","url":null,"abstract":"This paper proposes a simple and a robust method to detect and extract the silhouettes from a video sequence of a static camera based on background subtraction technique. The proposed method analyse the pixel history as a time series observations. A robust technique to detect motion based on kernel density estimation is presented. Two consecutive stages of the k-means clustering algorithm are utilized to identify the most reliable background regions and decrease false positives. Pixel and object based updating mechanism is presented to cope with challenges like gradual and sudden illumination changes, ghost appearance, and non-stationary background objects. Experimental results show the efficiency and the robustness of the proposed method to detect and extract silhouettes for outdoor and indoor environments.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126272285","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 : 2011-11-01DOI: 10.1109/ACPR.2011.6166683
Qian Chen, Haiyuan Wu, H. Taki
This paper describes an effective method for detecting multiple symmetric objects in an image. A “pseudo-affine invariant SIFT” is used for detecting symmetric feature pairs in perspective images. Candidates of symmetric axes are estimated from every two symmetric feature pairs, and the one supported by the most symmetric feature pairs is detected as the most relevant symmetric axis of a symmetric object. The symmetric feature pairs supporting the symmetric axis are then used to detect other symmetric axes in the same symmetric object. This procedure is applied repeatedly to the symmetric feature pairs after eliminating the ones that support the already detected symmetric axes to detect all symmetric objects in the image. The effectiveness of this method has been confirmed through several experiments using real images and common image databases.
{"title":"Detecting multiple symmetries with extended SIFT","authors":"Qian Chen, Haiyuan Wu, H. Taki","doi":"10.1109/ACPR.2011.6166683","DOIUrl":"https://doi.org/10.1109/ACPR.2011.6166683","url":null,"abstract":"This paper describes an effective method for detecting multiple symmetric objects in an image. A “pseudo-affine invariant SIFT” is used for detecting symmetric feature pairs in perspective images. Candidates of symmetric axes are estimated from every two symmetric feature pairs, and the one supported by the most symmetric feature pairs is detected as the most relevant symmetric axis of a symmetric object. The symmetric feature pairs supporting the symmetric axis are then used to detect other symmetric axes in the same symmetric object. This procedure is applied repeatedly to the symmetric feature pairs after eliminating the ones that support the already detected symmetric axes to detect all symmetric objects in the image. The effectiveness of this method has been confirmed through several experiments using real images and common image databases.","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129295517","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 : 2011-11-01DOI: 10.1109/acpr.2011.6166550
Jiewei Wang, Yunhong Wang, Zhaoxiang Zhang
{"title":"Interesting region detection in aerial video using Bayesian topic models","authors":"Jiewei Wang, Yunhong Wang, Zhaoxiang Zhang","doi":"10.1109/acpr.2011.6166550","DOIUrl":"https://doi.org/10.1109/acpr.2011.6166550","url":null,"abstract":"","PeriodicalId":287232,"journal":{"name":"The First Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860536","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：