Multi-component cross entropy segmentation for color image retrieval
This paper presents an adaptive color image segmentation method based on cross entropy minimization. This method is a multi-component approach and provides a hierarchical partitioning of the 3D color space using spherical neighbourhoods. The number of dominant colors (classes) issued from this segmentation is automatically estimated. This avoids an a priori estimation of the number of final classes. The segmentation method is then applied for image retrieval purposes. Local and global descriptors are defined in order to characterize the color feature of these classes. The local descriptors provide information about the local activity in the image class per class, and the global ones evaluate the qualitative image content. Their combination increases significantly the performance of the image retrieval system presented in this paper.