Affiliation:
1. Chongqing University, China
Abstract
In the research of Web content-based image retrieval, how to reduce more of the image dimensions without losing the main features of the image is highlighted. Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. This paper starts with analysis of commonly used methods for the dimension reduction of Web images, followed by a new algorithm for nonlinear dimensionality reduction based on the HSV image features. The approach obtains intrinsic dimension estimation by similarity calculation of two images. Finally, some improvements were made on the Parallel Genetic Algorithm (APGA) by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. Experimental results illustrate the validity of the algorithm.
Reference36 articles.
1. Bai, X., & Liu, W. J. (2009). Research of image retrieval based on color. In Proceedings of the International Forum on Computer Science-Technology and Applications, Chongqing, China (Vol. 2, pp. 283-286).
2. Content-based image retrieval using visually significant point features
3. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
4. Charting a manifold.;M.Brand;Advances in Neural Information Processing Systems,2003
5. Intrinsic dimensionality estimation with optimally topology preserving maps