Current challenges in content based image retrieval by means of low-level feature combining

The aim of this paper is to discuss a fusion of the two most popular low-level image features - colour and shape - in the aspect of content-based image retrieval. By combining them we can achieve much higher accuracy in various areas, e.g. pattern recognition, object representation, image retrieval. To achieve such a goal two general strategies (sequential and parallel) for joining elementary queries were proposed. Usually they are employed to construct a processing structure, where each image is being decomposed into regions, based on shapes with some characteristic properties - colour and its distribution. In the paper we provide an analysis of this proposition as well as the exemplary results of application in the Content Based Image Retrieval problem. The original contribution of the presented work is related to different fusions of several shape and colour descriptors (standard and non-standard ones) and joining them into parallel or sequential structures giving considerable improvements in content-based image retrieval. The novelty is based on the fact that many existing methods (even complex ones) work in single domain (shape or colour), while the proposed approach joins features from different areas.