Dimensionality reduction using multidimensional scaling for image search

Morris Beatty and B.S. Manjunath
Department of Electrical and Computer Engineering,
University of California, Santa Barbara, CA 93106-9560.
E-mail: mlbeatty [at] iplab.ece.ucsb.edu, manj [at] ece.ucsb.edu


There has been much interest recently in image content based retrieval, with applications to digital libraries and image database accessing. One approach to this problem is to base retrieval from the database upon feature vectors which characterize the image texture. Since feature vectors are often high dimensional, Multi-Dimensional Scaling, or non-Linear Principal Components Analysis (PCA) may be useful in reducing feature vector size, and therefore computation time. We have investigated a variant of the non-linear PCA algorithm described in 6 and its usefulness in the database retrieval problem. The results are quite impressive: in an experiment using an aerial photo database, feature vector length was reduced by a factor of 10 without significantly reducing retrieval performance.
[PDF] [BibTex]
M. Beatty and B. S. Manjunath,
IEEE International Conference on Image Processing, vol. 2, pp. 835-838, Santa Barbara, California, Oct. 1997.
Node ID: 261 , DB ID: 57 , VRLID: 48 , Lab: VRL , Target: Proceedings
Subject: [Multimedia Database Mining] « Look up more