| Computer Science and Technology |
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| Image feature optimization based on nonlinear dimensionality reduction |
| Rong ZHU, Min YAO |
| School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; School of Information Engineering, Jiaxing University, Jiaxing 314001, China; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China |
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Abstract Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between high- and low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.
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Received: 26 May 2009
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