基于SEF-GHEI及协同表示的步态识别研究

将协同表示方法应用于步态识别中可以解决稀疏表示方法计算耗时的问题,但提取步态特征采用的GEI算法没有考虑步态内部轮廓边界信息,导致识别率不高。针对此问题,本文提出使用融合Hog和GEI算法的方法提取步态特征,在此基础上使用协同表示的方法训练,再通过计算测试样本的最小重构误差进行分类。实验结果表明,该方法在单一视角下步态识别准确率平均提高了1.315%,以及跨视角下步态识别准确率平均提高了6.51%,说明本方法是可行的。     

HEp-2 cell pattern classification with discriminative dictionary learning

The paper presents a supervised discriminative dictionary learning algorithm specially designed for classifying HEp-2 cell patterns. The proposed algorithm is an extension of the popular K-SVD algorithm: at the training phase, it takes into account the discriminative power of the dictionary atoms and reduces their intra-class reconstruction error during each update. Meanwhile, their inter-class reconstruction effect is also considered. Compared to the existing extension of K-SVD, the proposed algorithm is more robust to parameters and has better discriminative power for classifying HEp-2 cell patterns. Quantitative evaluation shows that the proposed algorithm outperforms general object classification algorithms significantly on standard HEp-2 cell patterns classifying benchmark¹ and also achieves competitive performance on standard natural image classification benchmark.     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Near-duplicate document image matching: A graphical perspective

A near-duplicate document image matching approach characterized by a graphical perspective is proposed in this paper. Document images are represented by graphs whose nodes correspond to the objects in the images. Consequently, the image matching problem is then converted to graph matching. To deal with the instability of object segmentation, a multi-granularity object tree is constructed for a document image. Each level in the tree corresponds to one possible object segmentation, while different levels are characterized by various object granularities. Some graphs can be generated from the tree and the objects associated with each graph may be of different granularities. Two graphs with the maximum similarity are found from the multi-granularity object trees of the two near-duplicate document images which are to be matched. The encouraging experimental results have demonstrated the effectiveness of the proposed approach.     

Image clustering based on sparse patch alignment framework

Image clustering methods are efficient tools for applications such as content-based image retrieval and image annotation. Recently, graph based manifold learning methods have shown promising performance in extracting features for image clustering. Typical manifold learning methods adopt appropriate neighborhood size to construct the neighborhood graph, which captures local geometry of data distribution. Because the density of data points’ distribution may be different in different regions of the manifold, a fixed neighborhood size may be inappropriate in building the manifold. In this paper, we propose a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds. Specifically, we assume that for each data point there exists a small neighborhood in which only the points that come from the same manifold lie approximately in a low-dimensional affine subspace. Based on the patch alignment framework, we propose an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point. After that, the whole alignment strategy is utilized to build the manifold. Experiments are conducted on four real-world datasets, and the results demonstrate the effectiveness of the proposed method.     

A METHOD FOR GENERATING INDEPENDENT REALIZATIONS OF A MULTIVARIATE NORMAL STATIONARY AND INVERTIBLE ARMA(p, q) PROCESS

Abstract. A method for generating finite independent realizations of a normal multivariate stationary ARMA( p, q ) process is proposed. It is based on an AR (1) representation of an ARMA( p, q ) process allowing for an exact generation of the initial values of the simulation algorithm. Input facilities are supplied in order to assure stationarity and invertibility of the considered process.