A cognitive approach for texture analysis using neighbors-based binary patterns

The human brain receives images from the natural world and understands scenes, places and events quickly, outperforming the most advanced artificial vision system. Most of surfaces are textured in real life. Thus, In this paper, a novel texture analysis method has been proposed. The texture can be seen as a visual representation of complex patterns that lead to cognitive understanding of the environment. Our method is inspired from the Local Binary Pattern (LBP) method. The proposed Neighbor based Binary Pattern (NBP) extracts the local pattern from the texture using an analysis window. Each neighbor of the central pixel is thresholded by the next neighbor and encoded (starting from the top-left neighbor and going clockwise). Thus, the central pixel describes the relative pertinent information between its neighboring pixels. The rotation invariant version of the NBP method extracts patterns which are robust against rotation. For this, the encoding process starts always from the higher neighbor. The encoding process is applied on whole the original image in order to obtain the RINBP image. A histogram is calculated from the RINBP image to describe the texture. The size of the obtained histogram was reduced while keeping the relevant information. In the experiments, the performance of the proposed feature is evaluated on thirteen textured images from Brodatz texture album. It is shown that the RINBP method outperforms the earlier versions of the rotation-invariant LBP and the classical NBP method. This is due to its ability to extract the relative and relevant information from the local neighborhood.