# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 1 Introduction  
Along with the development of imaging equipment, a larger number of images have been extensively collected from various fields [1; 2; 3]. Meanwhile, CBIR technology has gradually become a hot research field, due to its applications in place recognition [4], image classification [5], and remote sensing [6]. Therefore, the problem of extracting effective, robust and practical features has attracted an increasing number of researchers. Thanks to these pioneers' breakthroughs, many approaches [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] have been continuously proposed and extended for the task of feature extraction. In the early days, a family of local binary pattern (LBP)-based methods [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] have been sequentially reported for the grayscale-based feature extraction. As a milestone, the LBP definition was initially authored by [PERSON] et al. [7], in which the referenced pixel and its nearest pixels were encoded as a binary string. Hereafter, [PERSON] et al. [8] extended the LBP to the local derivative pattern (LDP) descriptor for refining the magnitude difference. Subsequently, [PERSON] et al. [9] designed a variant of the LBP named the completed LBP, and it is used for improving the robustness to rotation. After that, the LBP variance was developed by [PERSON] et al. [10] for addressing the drawback of the global information loss. Further, [PERSON] et al. [11] introduced an improvement of the LBP named thelocal ternary pattern (LTP), and it was integrated with the kernel principal component analysis to improve the robustness to illumination. After, [PERSON] et al. [12] modified the LTP to the local tetra pattern (LTrP), in which the referenced pixel and its surrounding pixels were computed by comparing the vertical and horizontal direction differences. Afterwards, [PERSON] et al. [13] converted the LTP to the local maximum edge binary patterns (LMEBP), and the LMEBP was combined with the Gabor transform for CBIR and object tracking applications. In order to extend the LBP to the dynamic-texture application, [PERSON] et al. [14] studied the LBP histogram Fourier (LBP-HF) for video sequence recognition. Further, in order to improve the robustness to noise, [PERSON] et al. [15] proposed the noise-resistant local binary pattern (NRLBP) to extract the local structure information. In order to retrieve the magnetic and resonance and computer tomography images, [PERSON] et al. [16] studied the local ternary co-occurrence patterns (LTCoP) encoding. Motivated by the concatenation strategy, [PERSON] et al. [17] used the LBP feature map and the local neighborhood difference pattern (LNDP) to integrate the binary information into the local intensity difference for the natural scene and texture retrieval applications. Nevertheless, since all the above approaches are limited to the grayscale-based feature extraction, the color information unavoidably is lost. In recent years, a family of color LBP descriptors [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] have been continuously explored for the color-based feature extraction. Among them, inspired by inter- and intra- channel encoding mechanisms, [PERSON] et al. [18] constructed the opponent color local binary patterns (OCLBP) for color textural classification. After that, [PERSON] et al. [19] proposed an extension of OCLBP named the improved opponent color local binary patterns (IOCLBP), in which point-to-average thresholding replaced point-to-point thresholding. Further, the grayscale-based LTrP was extended to the local oppugnant color texture pattern (LOCTP) by [PERSON] et al. [20], and the LOCTP was extracted from the RGB, YCbCr, and HSV color models respectively. Inspired by the pair-based strategy, [PERSON] et al. [21] presented the pairwise rotation-invariant co-occurrence LBP (PRICoLBP) by incorporating the R, G and B components. Similarly, [PERSON] et al. [22] proposed the pairwise cross pattern (PCP), in which the color and LBP information were combined in pairwise and cross manners.