Great amount of data under varying intrinsic features are empirically thought of as high-dimensional nonlinear manifold in the observation space. With respect to different categories, we present two recognition approaches, i.e. the combination of manifold learning algorithm and linear discriminant analysis (MLA+LDA), and nonlinear auto-associative modeling (NAM). For similar object recognition, e.g. face recognition, MLA + LDA is used. Otherwise, NAM is employed for objects from largely different categories. Experimental results on different benchmark databases show the advantages of the proposed approaches.
CITATION STYLE
Zhang, J., Li, S., Wang, J., Tan, Y.-P., Yap, K. H., & Wang, L. (2005). Intelligent Multimedia Processing with Soft Computing. Intelligent Multimedia Processing with Soft Computing, Studies in Fuzziness and Soft Computing, Volume 168/2005 (Vol. 168, pp. 281–300). Retrieved from http://www.springerlink.com/content/67703r1qk2732wg8/
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