Learning completed discriminative local features for texture classification. (July 2017) - Zhang, Zhong; Liu, Shuang; Mei, Xing; Xiao, Baihua; Zheng, Liang
Highlights: We propose a novel feature representation method, namely Completed Discriminative Local Features (CDLF), for texture classification. The CDLF learn transformation matrices for texture images that maximize the mutual information between the local features and their category labels. We propose an adaptive histogram accumulation (AHA) algorithm, which leverages the local contrast characteristic in the process of histogram accumulation. The CDLF achieves higher accuracy than state-of-the-art methods on three databases. Abstract: Local binary patterns (LBP) and its variants have shown great potentials in texture classification tasks. LBP-like texture classification methods usually follow a two-step feature extraction process: in the first pattern encoding step, the local structure information around each pixel is encoded into a binary string; in the second histogram accumulation step, the binary strings are accumulated into a histogram as the feature vector of a texture image. The performances of these classification methods are closely related to the distinctiveness of the feature vectors. In this paper, we propose a novel feature representation method, namely Completed Discriminative Local Features (CDLF), for texture classification. The proposed CDLF improves the distinctiveness of LBP-like feature vectors in two aspects: in the pattern encoding stage, we learn a transformation matrix using labeled data, which significantly increases the discrimination power of the encoded binary strings; in the histogram accumulation step, we use an adaptive weight strategy to consider the contributions of pixels in different regions. The experimental results on three challenging texture databases demonstrate that the proposed CDLF achieves significantly better results than previous LBP-like feature representation methods for texture classification tasks.