Local derivative radial patterns: A new texture descriptor for content-based image retrieval

Abstract

In this paper, we propose a novel local pattern descriptor called Local Derivative Radial Pattern (LDRP) for texture representation in content-based image retrieval. All prior local patterns are based on gray-level difference of pixels located in a square or circle. Since many of the actual textures can be represented by intensity relationship of pixels along a line, these methods do not have a suitable ability to represent texture information. In prior methods, difference between referenced pixel and its adjacent pixel is encoded with two, three or four values which leads to information loss of the image. The proposed LDRP is based on gray-level difference of pixels along a line and their weighted combinations. In addition, multi-level coding in different directions is used instead of binary coding. The performance of the proposed method is compared with prior methods including local binary pattern (LBP), local ternary pattern (LTP), local derivative pattern (LDP), local tetra pattern (LTrP) and local vector pattern (LVP). The proposed LDRP outperforms all mentioned prior methods by at least 3.82% and 5.17% in terms of average precision on Brodatz and VisTex databases, respectively.