A DLSI approach for content-based image classification

Clustering images into semantically meaningful clusters using low-level visual features is a demanding and important problem in content-based image retrieval. In this paper, we investigate the feasibility of a DLSI (differential latent semantic indexing) approach in image classification. The new method applies a combined use of the projections on and the distances to the DLSI space from a differential "image" of any two images, and employs a posteriori likelihood function in measuring the similarity between an image class in the database and an image of query. Our simple experiment gives a supporting evidence of the strength of DLSI approach in capturing the intricate variability of image content contributing to a more robust context contingent classification method.