Image segmentation based on the integration of colour–texture descriptors—A review

The adaptive integration of the colour and texture attributes in the development of complex image descriptors is one of the most investigated topics of research in computer vision. The substantial interest shown by the research community in colour–texture-based segmentation is mainly motivated by two factors. The first is related to the observation that the imaged objects are often described at perceptual level by distinctive colour and texture characteristics, while the second is motivated by the large spectrum of possible applications that can be addressed by the colour–texture integration in the segmentation process. Over the past three decades a substantial number of techniques in the field of colour–texture segmentation have been reported and it is the aim of this article to thoroughly evaluate and categorise the most relevant algorithms with respect to the modality behind the integration of these two fundamental image attributes. In this paper we also provide a detailed discussion about data collections, evaluation metrics and we review the performance attained by state of the art implementations. We conclude with a discussion that samples our views on the field of colour–texture image segmentation and this is complemented with an examination of the potential future directions of research. ⺠To review and categorise the published research work in colour–texture segmentation. ⺠To discuss the main trends behind the colour–texture integration process. ⺠To discuss the metrics currently in use for segmentation performance evaluation. ⺠To review the publicly available colour–texture image databases. ⺠To analyse and quantify the performance of state of the art implementations.