Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Abstract

Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to decrease the computational cost, the proposed scheme consists of a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed scheme. Experimental results and comparisons show that the proposed CBIR system performs efficiently.