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Neighborhood Structure Preserving Projection And Its Application

Posted on:2012-12-17Degree:MasterType:Thesis
Country:ChinaCandidate:Y Y LiFull Text:PDF
GTID:2178330332487921Subject:Communication and Information System
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Face recognition is one of the representative biometric feature recognition techniques and has becoming an active research field in pattern recognition and computer vision. In face recognition, feature extraction is one of the very important key steps and involves the performance of the subsequent anslysis and classification system. So, how to effectively extract the intrinsic geometric structure embedded in data, which facilitates the subsequent analysis such as classification, has become one of the key problems to be solved. Many approaches have been proposed to slove it, and one of the most active approaches is subspace analysis methods (SAM). The dissertation mainly studies how to characterize the spatial geometric structures including similarity and diversity of data by combining graph theory and manifold learning belongs to SAM. The main contributions and work are as follows:1. Neighborhood Structure Preserving Embedding (NSPE) is proposed. NSPE defines two adjacency graphs, namely similarity graph and diversity graph, over the training data to model the spatial similarity and diversity structures of data, respectively. Similarity scatter and diversity scatter are calculated from the two graphs. Based on the two scatters, a concise feature extraction criterion is then raised by maximizing the ratio of the diversity scatter to similarity scatter.2. Two-dimensional Neighborhood Structure Preserving Embedding(2DNSPE) is proposed. 2DNSPE directly calculates the similarity scatter and diversity scatter from the image matrices. Based on the two scatters, a concise feature extraction criterion is raised by maximizing the ratio of the diversity scatter to similarity scatter. Different from NSPE, 2DNSPE avoids transforming the image matric into a vector, thus reducing the computational complexity and alleviating the small sample problem.3. Two novel methods, namely Directional Two-dimensional Neighborhood Preserving Embedding (Dir-2DNPE) and Directional Two-dimensional Neighborhood Structure Preserving Embedding (Dir-2DNSPE), are proposed to reduce dimension of images. Two methods rearrange pixels in images by using directional image and then performe Dir-2DNPE and Dir-2DNSPE to reduce dimension of images respectively. Experiment results show the efficiency of the two methods.
Keywords/Search Tags:Manifold learning, Diversity geometric structure, Similarity geometric structure, Feature extraction, Face recognition
PDF Full Text Request
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