Locality-sensitive kernel sparse representation classification for face recognition

In this paper a new classification method called locality-sensitive kernel sparse representation classification (LS-KSRC) is proposed for face recognition. LS-KSRC integrates both sparsity and data locality in the kernel feature space rather than in the original feature space. LS-KSRC can learn more discriminating sparse representation coefficients for face recognition. The closed form solution of the l₁-norm minimization problem for LS-KSRC is also presented. LS-KSRC is compared with kernel sparse representation classification (KSRC), sparse representation classification (SRC), locality-constrained linear coding (LLC), support vector machines (SVM), the nearest neighbor (NN), and the nearest subspace (NS). Experimental results on three benchmarking face databases, i.e., the ORL database, the Extended Yale B database, and the CMU PIE database, demonstrate the promising performance of the proposed method for face recognition, outperforming the other used methods.     

Modeling and recognizing action contexts in persons using sparse representation

This paper proposes a novel dynamic sparsity-based classification scheme to analyze various interaction actions between persons. To address the occlusion problem, this paper represents an action in an over-complete dictionary to makes errors (caused by lighting changes or occlusions) sparsely appear in the training library if the error cases are well collected. Because of this sparsity, it is robust to occlusions and lighting changes. In addition, a novel Hamming distance classification (HDC) scheme is proposed to classify action events to various types. Because the nature of Hamming code is highly tolerant to noise, the HDC scheme is also robust to environmental changes. The difficulty of complicated action modeling can be easily tackled by adding more examples to the over-complete dictionary. More importantly, the HDC scheme is very efficient and suitable for real-time applications because no minimization process is involved to calculate the reconstruction error.     

Action recognition on motion capture data using a dynemes and forward differences representation

In this paper we introduce a novel method for action/movement recognition in motion capture data. The joints orientation angles and the forward differences of these angles in different temporal scales are used to represent a motion capture sequence. Initially K-means is applied on training data to discover the most representative patterns on orientation angles and their forward differences. A novel K-means variant that takes into account the periodic nature of angular data is applied on the former. Each frame is then assigned to one or more of these patterns and histograms that describe the frequency of occurrence of these patterns for each movement are constructed. Nearest neighbour and SVM classification are used for action recognition on the test data. The effectiveness and robustness of this method is shown through extensive experimental results on four standard databases of motion capture data and various experimental setups.     

Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition

Much of the existing work on action recognition combines simple features with complex classifiers or models to represent an action. Parameters of such models usually do not have any physical meaning nor do they provide any qualitative insight relating the action to the actual motion of the body or its parts. In this paper, we propose a new representation of human actions called sequence of the most informative joints (SMIJ), which is extremely easy to interpret. At each time instant, we automatically select a few skeletal joints that are deemed to be the most informative for performing the current action based on highly interpretable measures such as the mean or variance of joint angle trajectories. We then represent the action as a sequence of these most informative joints. Experiments on multiple databases show that the SMIJ representation is discriminative for human action recognition and performs better than several state-of-the-art algorithms.     

改进相关反馈技术在CBVR人体动作识别中的应用研究

针对CBVR人体动作识别问题,提出利用相关反馈技术与其它技术相结合。首先,分析了从视频中提取人类行为并将其表示成特征集的方法;然后研究了各种基于内容的视频检索方法;最后,将这些技术进行组合得到基于相关反馈技术的最优CBVR人体动作识别方法。在三个行为数据库中(包括UCF运动,UCF视频网站和HOHA2)评估了由上述几种技术组合而成的性能,实验结果为开发高效的基于内容的视频检索系统提供了有益的借鉴。