The Semantic Clustering of Images and Its Relation with Low Level Color Features

Content-based image retrieval - CBIR uses visual content (low-level features) of images such as color, texture, shape, etc. to representand to index images. Extensive experiments on CBIR show that low-level features not represent exactly the high-level semantic concepts and can fail when used to retrieve similar images. In order to overpass this problem, different approaches aim to propose new methods that use different techniques combined with low-level descriptors. In this work, we analyze the relation between low-level color features and the high-level features to justify or not the use of these descriptors in the CBIR process. In this sense, a group of users were asked about the similarity of a group of images. After, Semantic clusters were established based on their answers. These clusters are compared with the classification obtained by color descriptors of the MPEG-7 standard, giving us an idea about the situations in which these low level color features can be used for CBIR and properties of their application.