Dimensionality Reduction for Image Retrieval

Dimensionality reduction methods are of interest in applications such as content based image and video retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve the nearest neighbors of a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. We investigate the use of weighted multi-dimensional scaling (WMDS) for dimensionality reduction. The main objective of the WMDS is to preserve the local topology of the high dimensional space, i.e., to map the nearest neighbors in the high dimensional space to nearest neighbors in the lower dimensional space. In addition to the well known retrieval accuracy as a measure of performance, we propose two additional measures that take into account the ordinal relationships among the nearest neighbors. Experimental results are given.