A backpropagation learning system for content based image retrieval

Abstract

In this paper we have proposed a Neural Network based architecture for content based image retrieval. To enhance the capabilities of proposed technique, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this we have used wavelet packets and Gabor filters features for repository image representation. To ensure the semantically identical image retrieval, a partial supervised learning based association scheme is presented, which guarantees the retrieval of images in an efficient way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems. © 2012 Springer-Verlag Berlin Heidelberg.