A new method of combining colour, texture and shape features using the genetic algorithm for image retrieval

Abstract

Semi-automatic or automatic image indexation emerged because manual image indexation is slow and tedious. Generally, this first indexation is used as part of a content-based image retrieval system (CBIR). To have a powerful CBIR system, it is necessary to be concerned with three main facets: 1) the choice of the descriptors (based on shape, colour and texture and/or a combination between them); 2) the process of indexation and finally; 3) the retrieval process. In this work, we focus mainly on an indexing based on genetic algorithm and particle swarm optimisation (PSO) algorithm. We chose an optimal combination of colour, shape and texture (PCM: powerful combination method) descriptors. The fruit of our research work is implemented in a system called image search engine (ISE) which showed a very promising performance. In fact, the performance evaluation of the PCM method of our descriptors combination showed upgrades of the average precision metric from 66.6% to 89.30% for the 'food' category colour histogram, from 77.7% to 100% concerning CCV for the 'flower' category, and from 44.4% to 87.65% concerning the co-occurrence matrix for the 'building' category using the Corel dataset. Likewise, our ISE system showed much more interesting performance compared to what was shown in previous works.