An Environment to Test Progressive Refinement of Indexing for Content-Based Image Retrieval

Content-based image retrieval is a fairly new discipline. Yet research in this field has highlighted many approaches that show good performance in specific subproblems using single filtering criteria, such as color based histograms, shape description or texture analysis. Most systems built so far use multiple access methods although they compose them according to a pre-defined strategy. In this paper we report an environment that allows the evaluation of different type of filtering composition strategies. The aim is to let the developers work with a quick tool for fine tuning of sequences or compositions of methods, especially when such methods are based on different features. The selectivity of each indexing method is tested against a single database of images and then the overall performance of a sequence or a composition of methods is quickly estimated. The experiments have been applied on filtering criteria that share a multiresolution approach, in order to highlight the role that the hierarchical indexing approach can play in reducing the overall computational cost.