New Texture Descriptor Based on Improved Orthogonal Difference Local Binary Pattern

Abstract

Local descriptor plays an important role in Content-Based Image Retrieval (CBIR) and face recognition. Almost all local patterns are based on the relationship between neighboring pixels in a local area. The most famous local pattern is Local Binary Pattern (LBP), in which the patterns are defined based on the intensity difference between a central pixel and its neighboring in a $3\times 3$ local window. Orthogonal Difference Local Binary Pattern (OLDBP) is an extended version of LBP which is introduced recently. In this paper, ODLBP is improved. In the proposed method each $3\times 3$ local window is divided into two groups and then local patterns of each group are extracted and finally, the feature vector is provided by concatenating of groups patterns. To evaluate the proposed method, three datasets Yale, ORL and GT are used. Implementation results show the powerful of the proposed method comparing to ODLBP. The proposed method is more faster than the ODLBP while its precision and recall are slightly higher than the ODLBP method.