Robust Texture Image Representation by Scale Selective Local Binary Patterns

Abstract

Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition applications, such as texture recognition. It could effectively address grayscale and rotation variation. However, it failed to get desirable performance for texture classification with scale transformation. In this paper, a new method based on dominant LBP in scale space is proposed to address scale variation for texture classification. First, a scale space of a texture image is derived by a Gaussian filter. Then, a histogram of pre-learned dominant LBPs is built for each image in the scale space. Finally, for each pattern, the maximal frequency among different scales is considered as the scale invariant feature. Extensive experiments on five public texture databases (University of Illinois at Urbana-Champaign, Columbia Utrecht Database, Kungliga Tekniska Högskolan-Textures under varying Illumination, Pose and Scale, University of Maryland, and Amsterdam Library of Textures) validate the efficiency of the proposed feature extraction scheme. Coupled with the nearest subspace classifier, the proposed method could yield competitive results, which are 99.36%, 99.51%, 99.39%, 99.46%, and 99.71% for UIUC, CUReT, KTH-TIPS, UMD, and ALOT, respectively. Meanwhile, the proposed method inherits simple and efficient merits of LBP, for example, it could extract scale-robust feature for a 200×200 image within 0.24 s, which is applicable for many real-time applications.

From BoW to CNN: Two Decades of Texture Representation for Texture Classification

“…A large number of LBP variants have been proposed to improve its robustness and to increase its discriminative power and applicability to different types of problems, and interested readers are referred to excellent surveys [86,132,185]. Recently, although CNN based methods are beginning to dominate, LBP research remains active, as evidenced by significant recent work [77,219,202,116,136,251,259,49].…”

Scale selective extended local binary pattern for texture classification

“…In addition, the selection of R in the calculation of ELBP RD also depends on R. Table 1. We notice that the best classification accuracy of the proposed method on the KTH-TIPS database is 98.11% with radius selection (2,3,4,7). When N equals four or five, we can obtain higher accuracy with smaller deviations.…”

“…To further increase the robustness of the proposed method on texture images with scale variations, inspired by the SSLBP framework in [7], we build the scale space using Gaussian filter. We first normalize image I to ensure normalized imagê I has zero mean and unit variance.…”

“…The work of [6] introduced a feature extraction method that implements scale-invariance by estimating local scales and normalizing local regions, but has high computational complexity. To improve efficiency, Guo et al [7] proposed the scale-selective local binary pattern (SSLBP) that first extracts scale-sensitive local features and then applies a global operator to achieve scale-invariance. In [7], the scale-invariant feature extraction scheme achieves good texture classification performance on texture databases with scale variations such as KTH-TIPS [8] and UMD [9].…”

Texture retrieval using periodically extended and adaptive curvelets

“…Several authors [12][13][14][15][16][17][18][19][20][21][22] have developed texture-retrieval algorithms based on spatial domain content analysis. Ojala et al [12] developed local binary patterns (LBP) that decompose input images into sets of coefficients representing intensity differences between a reference pixel and its neighbors.…”