Reducing the Dimensions of Texture Features for Image Retrieval Using Multi-layer Neural Networks

This paper presents neural network-based dimension reduction of texture features in content-based image retrieval. In particular, we highlight the usefulness of hetero-associative neural networks to this task, and also propose a scheme to combine the hetero-associative and auto-associative functions. A multichannel Gabor-filtering approach is used to derive 30-dimensional texture features from a set of homogeneous texture images. Multi-layer feedforward neural networks are then trained to reduce the number of feature dimensions. Our results show that the methods lead to a reduction of up to 30% while keeping or even improving the performance of similarity ranking. This has the benefit of alleviating the ill-effects of the high dimensionality of features in current image indexing methods and resulting in significant speeding up retrieval rates. Results using principal component analysis are also provided for comparison. Receiveed: 6 July 1998?,Received in revised form: 6 November 1998?Accepted: 15 December 1998