Local Binary Patterns of Segments of a Binary Object for Shape Analysis

The paper presents an effective, robust and geometrically invariants, collection of contours or boundaries base local binary pattern (LBP) for binary object shape retrieval and classification. The contours segmentation or deformations of an object is a preprocessing step of shape retrieval and classification that segment the binary object shape in a shape-preserving sequence of contours segment using a coordination number shape segmentation approach. The proposed local binary pattern extracts the minimum decimal value corresponding to the pattern of object contour points for each and every contours segment. It is one of the most important features in content-based image retrieval. At the matching stage, we find Euclidean distance between eigenvalues of correlation coefficient of Hu’s seven moments corresponding to each contour segment for given two objects. The LBP pattern corresponding to the image contour provides excellent power, which is demonstrated by excellent retrieval performance on several popular shape benchmarks, including MPEG-7 CE-Shape-1 dataset and Kimia’s dataset. Experimental results obtained from popular databases demonstrate that the proposed linear binary pattern can achieve comparably better results than existing algorithms.