Enhancing CBIR Performance Using Evolutionary Algorithm-Assisted Significant Feature Selection: A Filter Approach

Abstract

Efficient searching becomes essential for large image archive, with more and more digital images available on Internet. Consequently, content-based image retrieval (CBIR) has drawn widespread research attentiveness in the last decade in the field of image processing, pattern recognition and computer vision. CBIR approach boils down to two core problems: feature extraction followed by feature matching. Feature selection is a process that selects pertinent features as a subset of original extracted features. This paper presents six filter approaches for significant features’ selection: decision tree, relief, genetic algorithm (GA) with correlation based feature selection (CFS) as fitness function, particle swarm optimisation (PSO) with CFS as fitness function, exhaustive search and forward selection with CFS as attribute subset evaluator. Work is carried out on publicly available Corel data set images. Experimental results prove that the features selected from PSO and GA with CFS enhances CBIR performance both in terms of higher retrieval accuracy and reduced computational time.