Adaptive filtering and indexing for image databases

In this paper we combine image feature extraction with indexing techniques for efficient retrieval in large texture images databases. A 2D image signal is processed using a set of Gabor filters to derive a 120 component feature vector representing the image. The feature components are ordered based on the relative importance in characterizing a given texture pattern, and this facilitates the development of efficient indexing mechanisms. We propose three different sets of indexing features based on the best feature, the average feature and a combination of both. We investigate the tradeoff between accuracy and discriminating power using these different indexing approaches, and conclude that the combination of best feature and the average feature gives the best results.