Toward Semantically Meaningful Feature Spaces for Efficient Indexing in Large Image Databases

Abstract

The optimized distance-based access methods currently available for multimedia databases are based on two major assumptions: a suitable distance function is known a priori, and the dimensionality of image features is low. The standard approach to building image databases is to represent images via vectors based on low-level visual features and make retrieval based on these vectors. However, due to the large gap between the semantic notions and low-level visual content, it is extremely difficult to define a distance function that accurately captures the similarity of images as perceived by humans. Furthermore, popular dimension reduction methods suffer from either the inability to capture the nonlinear correlations among raw data or very expensive training cost. To address the problems, in this chapter we introduce a new indexing technique called Combining Multiple Visual Features (CMVF) that integrates multiple visual features to get better query effectiveness. Our approach is able to produce low-dimensional image feature vectors that include not only low-level visual properties but also high-level semantic properties. The hybrid architecture can produce feature vectors that capture the salient properties of images yet are small enough to allow the use of existing high-dimensional indexing methods to provide efficient and effective retrieval.