An incremental evolutionary learning method for optimizing content-based image indexing algorithms

One of the future directions of content-based image retrieval (CBIR) systems is incremental learning of indexing and retrieval algorithms. Optimization of the indexing algorithm is more difficult compared to the retrieval algorithm enhancement; since each time the indexing algorithm parameters are modified, all images of the reference database should be indexed again. This paper considers, for the first time, a challengeable limitation of actual indexing optimization systems: learning in dynamic and incremental CBIR environments. We introduce a new incremental evolutionary optimization method based on evolutionary group algorithm. The new incremental evolutionary group algorithm (IEGA) overcomes time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing optimization tasks and presents a new strategy that is enhanced with the ability of incremental learning. Evaluation results on some simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.