Transformation of compressed domain features for content-based image indexing and retrieval

In this paper, we address the problem of image content characterization in the compressed domain for the facilitation of similarity matching in content-based image retrieval. Specifically, given the disparity of the content characterization power of compressed domain approaches and those based on pixel-domain features, with the latter being usually considered as the more superior one, our objective is to transform the selected set of compressed domain feature histograms in such a way that the retrieval result based on these features is compatible with their spatial domain counterparts. Since there are a large number of possible transformations, we adopt a genetic algorithm approach to search for the optimal one, where each of the binary strings in the population represents a candidate transformation. The fitness of each transformation is defined as a function of the discrepancies between the spatial-domain and compressed-domain retrieval results. In this way, the GA mechanism ensures that transformations which best approximate the performance of spatial domain retrieval will survive into the next generation and are allowed through the operations of crossover and mutation to generate variations of themselves to further improve their performances.