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International Conference on Pattern Recognition

作者： Cheng, Zhongwei Qin, Lei Huang, Qingming Jiang, Shuqiang Tian, Qi Graduate University of Chinese Academy of Sciences Beijing 100190 China Key Lab. of Intelli. Info. Process Inst. of Comput. Tech. CAS Beijing 100190 China Department of Computer Science University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9780769541099

Human action recognition has been well studied recently, but recognizing the activities of more than three persons remains a challenging task. In this paper, we propose a motion trajectory based method to classify human group activities. Gaussian Processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability of people's activities in group. With respect to the relationships of persons in group activities, three discriminative descriptors are designed, which are Individual, Dual and Unitized Group Activity Pattern. We adopt the Bag of Words approach to solve the problem of unbalanced number of persons in different activities. Experiments are conducted on the human group-activity video database, and the results show that our approach outperforms the state-of-the-art. © 2010 IEEE.

关键词： Gaussian distribution

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Action recognition using spatial-temporal context

Action recognition using spatial-temporal context

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International Conference on Pattern Recognition

作者： Hu, Qiong Qin, Lei Huang, Qingming Jiang, Shuqiang Tian, Qi Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100190 China Department of Computer Science University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9780769541099

The spatial-temporal local features and the bag of words representation have been widely used in the action recognition field. However, this framework usually neglects the internal spatial-temporal relations between video-words, resulting in ambiguity in action recognition task, especially for videos "in the wild". In this paper, we solve this problem by utilizing the volumetric context around a video-word. Here, a local histogram of video-words distribution is calculated, which is referred as the "context" and further clustered into contextual words. To effectively use the contextual information, the descriptive video-phrases (ST-DVPs) and the descriptive video-cliques (ST-DVCs) are proposed. A general framework for ST-DVP and ST-DVC generation is described, and then action recognition can be done based on all these representations and their combinations. The proposed method is evaluated on two challenging human action datasets: the KTH dataset and the YouTube dataset. Experiment results confirm the validity of our approach. © 2010 IEEE.

关键词： Pattern recognition

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2010年 6108 LNCS卷

作者： Kratochvíl, Jan Li, Angsheng Fiala, Jiří Kolman, Petr Charles University Malostranské nám. 25 11800 Praha 1 Czech Republic State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

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S3MKL: Scalab.e semi-supervised multiple kernel learning for image data mining

S3MKL: Scalable semi-supervised multiple kernel learning for...

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Wang, Shuhui Jiang, Shuqiang Huang, Qingming Tian, Qi Key Lab. of Intell. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China Dept. of Computer Science Univ. of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781605589336

For large scale image data mining, a challenging problem is to design a method that could work efficiently under the situation of little ground-truth annotation and a mass of unlab.led or noisy data. As one of the major solutions, semi-supervised learning (SSL) has been deeply investigated and widely used in image classification, ranking and retrieval. However, most SSL approaches are not able to incorporate multiple information sources. Furthermore, no sample selection is done on unlab.led data, leading to the unpredictable risk brought by uncontrolled unlab.led data and heavy computational burden that is not suitable for learning on real world dataset. In this paper, we propose a scalab.e semi-supervised multiple kernel learning method (S3MKL) to deal with the first problem. Our method imposes group LASSO regularization on the kernel coefficients to avoid over-fitting and conditional expectation consensus for regularizing the behaviors of different kernel on the unlab.led data. To reduce the risk of using unlab.led data, we also design a hashing system where multiple kernel locality sensitive hashing (MKLSH) are constructed with respect to different kernels to identify a set of "informative" and "compact" unlab.led training subset from a large unlab.led data corpus. Combining S3MKL with MKLSH, the method is suitable for real world image classification and personalized web image re-ranking with very little user interaction. Comprehensive experiments are conducted to test the performance of our method, and the results show that our method provides promising powers for large scale real world image classification and retrieval. © 2010 ACM.

关键词： Image classification

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Nearest-neighbor classification using unlab.led data for real world image application

Nearest-neighbor classification using unlabeled data for rea...

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Wang, Shuhui Huang, Qingming Jiang, Shuqiang Tian, Qi Key Lab. of Intell. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China Dept. of Computer Science Univ. of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781605589336

Currently, Nearest-Neighbor approaches (NN) have been widely applied to real world image data mining. These approaches have the following three disadvantages: (i) the performance is inferior on small datasets;(ii) the performance of approximated nearest neighbor search will degrade for data with high dimensions;(iii) they are heavily dependent on the chosen feature and distance measure. To overcome these intrinsic weaknesses, we propose a novel Nearest-Neighbor method, which improves the original NN approaches from three aspects. Firstly, we propose a novel neighborhood similarity measure, where the similarity between test images and lab.led images in the database is calculated jointly by the original image-to-image similarity and the average similarity of their neighboring unlab.led data. Secondly, we adopt the kernelized locality sensitive hashing to effectively conduct the nearest neighbor search for high dimensional data. Finally, to enhance the robustness of the method on different genres of images, we propose to fuse the discrimination power of different features by considering all the retrieved nearest neighbors via hashing systems using different features/kernels. Experimental result shows the advantage over traditional Nearest-Neighbor methods using the lab.led data only. Even when the ratio of lab.led data is very small, our method could also achieve remarkable results, thanks to the help of unlab.led data and multiple features. © 2010 ACM.

关键词： Image classification

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An associative memory system for incremental learning and temporal sequence

An associative memory system for incremental learning and te...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Shen, Furao Yu, Hui Kasai, Wataru Hasegawa, Osamu State Key Laboratory for Novel Software Technology Jiangyin Information Technology Research Institute Nanjing University Nanjing 210093 China Department of Computer Intelligence and Systems Science Tokyo Institute of Technology Yokohama 226-8503 Japan Imaging Science and Engineering Lab. Tokyo Institute of Technology R2-52 4259 Nagatsuda Midori-ku Yokohama 226-8503 Japan

ISBN: (纸本)9781424469178

An associative memory (AM) system is proposed to realize incremental learning and temporal sequence learning. The proposed system is constructed with three layer networks: The input layer inputs key vectors, response vectors, and the associative relation between vectors. The memory layer stores input vectors incrementally to corresponding classes. The associative layer builds associative relations between classes. The proposed method can incrementally learn key vectors and response vectors;store and recall both static information and temporal sequence information;and recall information from incomplete or noise-polluted inputs. Experiments using binary data, real-value data, and temporal sequences show that the proposed method works well. © 2010 IEEE.

关键词： Vectors

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FECG extraction using Bayesian inference and neural networks approximation

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Journal of Computational Information Systems 2010年第6期6卷 1769-1778页

作者： Yin, Ying Ye, Mao Ren, Dongxiao Zhu, Yuanxiang Yang, Chenxue School of Applied Mathematics University of Electronic Science and Technology of China Chengdu 611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

Neural networks have received much attention in the extraction of fetal electrocardiogram signal in recent years. This paper provides a new method to extract the fetal electrocardiogram signal which uses Bayesian inference and neural networks approximation. We suppose there are two signals obtained from thoracic region and abdominal region of a pregnant woman. Due to the characteristics of Bayesian statistics, electrocardiogram signal can be modeled by generalized Gaussian distribution. Neural networks is used to approximate nonlinear function. The distorted thoracic signal is obtained by backpropagation algorithm because neural networks can uniformly approximate any continuous function if there is sufficient number of neurons in the hidden layer. Iterative technique is also used to update the fetal electrocardiogram signal. By using Bayesian inference and neural networks approximation, the fetal electrocardiogram signal is achieved. Simulations demonstrate that the effectiveness of our method. Copyright © 2010 Binary Information Press.

关键词： Extraction

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Automatic testing of Web services in Haskell platform

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Journal of Computational Information Systems 2010年第9期6卷 2859-2867页

作者： Zhang, Yingzhou Fu, Wei Qian, Junyan College of Computer Nanjing Univ. of Posts and Telecomm. Nanjing 210003 China State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecomm. Beijing 100876 China State Key Lab. of Novel Software Technology Nanjing Univ. Nanjing 210093 China Dept. of Computer Science and Technology Guilin Univ. of Electronic Technology Guilin 541004 China

As a concrete alternative to the development of distributed and connected applications, the Web Service (WS) technique is attracting more and more attention. WS testing is important because it will help the consumers to assess many WS quality attributes such as robustness. We in this paper present a framework with Haskell modules (such as QuickCheck, SmallCheck, HUnit and HSX) for automatically generating and invoking WS based on their WSDL specifications. Based on QuickCheck and HSX, we can quickly generate a number of WS tests and easily execute them with QuickCheck properties. With the help of HUnit, we can easily organize our WS test plan. In particular, a WS test suite can more easily be computed ″on the fly″ than in other test frameworks. Moreover, the nature of Haskell such as powerful abstraction facilities can provide unmatched support for WS regression testing. © 2010 Binary Information Press.

关键词： WSDL

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Building contextual visual vocabulary for large-scale image applications

Building contextual visual vocabulary for large-scale image ...

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Zhang, Shiliang Huang, Qingming Hua, Gang Jiang, Shuqiang Gao, Wen Tian, Qi Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China IBM Research T. J. Watson Center NY 10523 United States Peking University No. 5 Yiheyuan Road Beijing 100871 China Computer Science Depart. University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781605589336

Not withstanding its great success and wide adoption in Bag-of-visual Words representation, visual vocabulary created from single image local features is often shown to be ineffective largely due to three reasons. First, many detected local features are not stable enough, resulting in many noisy and non-descriptive visual words in images. Second, single visual word discards the rich spatial contextual information among the local features, which has been proven to be valuable for visual matching. Third, the distance metric commonly used for generating visual vocabulary does not take the semantic context into consideration, which renders them to be prone to noise. To address these three confrontations, we propose an effective visual vocabulary generation framework containing three novel contributions: 1) we propose an effective unsupervised local feature refinement strategy;2) we consider local features in groups to model their spatial contexts;3) we further learn a discriminant distance metric between local feature groups, which we call discriminant group distance. This group distance is further leveraged to induce visual vocabulary from groups of local features. We name it contextual visual vocabulary, which captures both the spatial and semantic contexts. We evaluate the proposed local feature refinement strategy and the contextual visual vocabulary in two large-scale image applications: large-scale near-duplicate image retrieval on a dataset containing 1.5 million images and image search re-ranking tasks. Our experimental results show that the contextual visual vocabulary shows significant improvement over the classic visual vocabulary. Moreover, it outperforms the state-of-the-art Bundled Feature in the terms of retrieval precision, memory consumption and efficiency. © 2010 ACM.

关键词： Semantics

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Building pair-wise visual word tree for efficent image re-ranking

Building pair-wise visual word tree for efficent image re-ra...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shiliang Zhang Qingming Huang Yijuan Lu Gao Wen Qi Tian Key Lab of Intelli. Info. Process Inst. of Comput. Tech. CAS Beijing China Chinese Academy and Sciences Beijing China Graduate University of Chinese Academy of Sciences Beijing China Department of Computer Science Texas State University San Marcos TX USA Department of Computer Science University of Texas at San Antonio TX USA

Bag-of-visual Words (BoW) image representation is getting popular in computer vision and multimedia communities. However, experiments show that the traditional BoW representation is not as effective as it is desired. One of the most important reasons for its ineffectiveness is that, the traditional BoW representation lost the spatial information in images. To overcome this problem, we propose the pair-wise visual word tree, within which each visual word keeps both the appearance and spatial information between two interest points in image. Thus, the corresponding novel BoW representation preserves the spatial structure in image. Based on the pair-wise visual word tree, we propose an efficient topic word selection algorithm, which utilizes the Latent Semantic Analysis to discover the most expressive visual words for different image categories. An efficient strategy is then utilized to combine the selected topic words for image re-ranking. Massive experiments show that the novel BoW representation shows promising performance. Meanwhile, the proposed image re-ranking strategy shows the state-of-the-art precision and promising efficiency.

关键词： Quantization Object recognition computer science Image analysis Content addressable storage Image representation computer vision Algorithm design and analysis Image processing Event detection

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