Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern

Abstract

Designing effective features is a fundamental problem in computer vision. However, it is usually difficult to achieve a great tradeoff between discriminative power and robustness. Previous works shown that spatial co-occurrence can boost the discriminative power of features. However the current existing co-occurrence features are taking few considerations to the robustness and hence suffering from sensitivity to geometric and photometric variations. In this work, we study the Transform Invariance (TI) of co-occurrence features. Concretely we formally introduce a Pairwise Transform Invariance (PTI) principle, and then propose a novel Pairwise Rotation Invariant Co-occurrence Local Binary Pattern (PRICoLBP) feature, and further extend it to incorporate multi-scale, multi-orientation, and multi-channel information. Different from other LBP variants, PRICoLBP can not only capture the spatial context co-occurrence information effectively, but also possess rotation invariance. We evaluate PRICoLBP comprehensively on nine benchmark data sets from five different perspectives, e.g., encoding strategy, rotation invariance, the number of templates, speed, and discriminative power compared to other LBP variants. Furthermore we apply PRICoLBP to six different but related applications-texture, material, flower, leaf, food, and scene classification, and demonstrate that PRICoLBP is efficient, effective, and of a well-balanced tradeoff between the discriminative power and robustness.

Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

“…3 illustrates that RI-LPH descriptors perform better than I-LPH descriptors. method dataset BRODATZ CURET LBP (Ojala et al, 2002) 97.1 93.3 SH (Liu and Wang, 2003) 84.6 86.4 LBP HF (Zhao et al, 2012) 97.4 90.6 PRI-CoLBP (Qi et al, 2012) 96.6 99.2 DeCAF (Cimpoi et al, 2014) 97.9 RI-LPH 98.0 95.6 I-LPH 94.4 89.4…”

“…However, PRI-CoLBP shows superior performance on CURET dataset. The difference between the performance of our algorithm and that of PRI-CoLBP for CURET dataset in Table 4 can be explained by noting that in (Qi et al, 2012) colour information is used in their classification, while our simulations are based on purely grey scale images. It is also noted that the SVM classifier used in (Qi et al, 2012) is generally more powerful than the kNN classifier employed here.…”

“…Texture analysis has been extensively studied in recent years and a large number of texture feature extraction techniques have been developed (Cohen et al, 1991;Nixon and Aguado, 2008;Varma and Zisserman, 2009;Zhao et al, 2012;Chen et al, 2010;Liu and Fieguth, 2012;Lei et al, 2014;Qi et al, 2012;Hinton and Salakhutdinov, 2006;Cimpoi et al, 2014;Simonyan et al, 2014). These methods can be roughly grouped into four main categories, namely statistical, structural, spectral and model based feature extraction methods (Xie and Mirmehdi, 2008).…”

Machine Vision in Food Recognition: Attempts to Enhance CBVIR Tools

“…methods based on keypoint-like feature detection and image matching) are, in general, applicable to food images. Three types of feature-based representations were eventually selected, namely SIFT, Bag of Textons ( [15]) and pairwise rotation invariant co-occurrence linear binary patterns ( [16]). …”

Texture Classification based on First Order Local Ternary Direction Patterns

“…It started by the revolutionary approach derived by Ojala et alto derive texture features by quantizing the local pixel values of a neighborhood in to two values and named it as local binary patterns (LBPs) [11,12]. Later several authors [13][14][15][16][17][18][19] carried out abundant work and derived efficient methods to further extend the benefits of LBP in various applications. The Binary features [12,13,15,20,21,22] gained reputation and recognition due to their efficient design, computational simplicity and good performance.…”