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

Manish Chowdhury, Sudeb Das, Malay Kumar Kundu

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.

Keywords

Features and Image Descriptors; Classification and Clustering; Feature Analysis; Learning; Colour and Texture; Image and Video Processing; Ripplet Transform, Color and Texture, Relevance Feedback, Content Based Image Retrieval, Artificial Neural Network, Multilayer Perceptron, Fuzzy

Full Text:

PDF (998Kb)
Copyright (c)