Morphological Color Size Distributions for Image Classification and Retrieval

Current content-based image retrieval techniques can typically perform efficient and effective searches on heterogeneous image databases. This contribution deals with an approach based on the integration of color and texture description which is applied to a very homogeneous database: a blood image bank. The content of images is very similar and therefore it becomes imperative to use very precise descriptors: the color is described by classical color distributions (histograms) and for the texture, we introduce the morphological color size distributions. The similarity is measured by computing distance metrics between histograms. In order to increase the accuracy of retrieval, the results of color-based and texture-based retrieval are integrated by combining the associated dissimilarity values. The effects of different integration methods on classification performance are explained by means of experimental tests in a database of 123 cell images (leukocyte color images). After learning processing, where different feature selection and classifier definition alternatives are tested, a definitive integrated approach is proposed (precision ).