COLOR CHILD: a novel color image local descriptor for texture classification and segmentation

Designing a robust image local descriptor for the purpose of image segmentation, analysis, recognition and classification has been an active area of research to date. In this paper, a robust and computationally efficient image local descriptor named “COLOR CHILD” has been proposed. COLOR CHILD addresses the weaknesses of Weber Local Descriptor (WLD) by considering Laplacian of Gaussian (LoG) for its differential excitation component and Tiansi fractional derivative-based filter for its orientation component. For any given image, these two components are then used to construct a concatenated histogram and with the addition of color moments up to third order the capabilities of the proposed descriptor COLOR CHILD has been extended to handle textures in color space. COLOR CHILD is shown to outperform all of the known state-of-the-art image local descriptors of parametric and non-parametric types on a variety of benchmark texture databases such as KTH-TIPS2-a, KTH-TIPS2-b, and CUReT under varying degrees of noise while performing texture classification task. Further, the response profile of the COLOR CHILD in terms of Wasserstein distance measures (obtained by sliding a query patch across the image to be segmented) is found to be better suited as initial image for active contour-based image and texture segmentation algorithms. The efficacy of the COLOR CHILD for segmentation task is amply demonstrated on synthetic color images under varying degrees of noise and on real-world texture images.