Local directional ternary pattern: A New texture descriptor for texture classification

khadiri, I. El; Chahi, Abderrazak; Merabet, Youssef El; Ruichek, Yassine; Touahni, Raja

doi:10.1016/j.cviu.2018.01.004

Cited by 79 publications

(21 citation statements)

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“…The LBP, which is developed by Ojala et al, is an effective gray-level texture operator that calculates the spatial characteristics of gray-level images [13], [14]. Many LBP based methods have been proposed to improve the performance of this method [29]. Thus, LBP methods with low computational complexity and high discrimination power have been developed.…”

Section: Previous Workmentioning

confidence: 99%

Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification

Alpaslan

Hanbay

2020

IEEE Access

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In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP. The Hessian matrix provides the directional derivative information of different texture regions, while ACS-LBP reveals the local texture features efficiently. To obtain the macro-and micro-structure textural changes, Hessian matrix is calculated in a multiscale schema. Multiscale Hessian matrix presents the intrinsic local geometry of the texture changes. The magnitude information of the Hessian matrix is used in the ACS-LBP method. A cross-scale joint coding strategy is used to construct Hess-ACS-LBP descriptor. Finally, histogram concatenation is carried out. Extensive experiments on eight texture databases of CUReT, USPTex, KTH-TIPS2b, MondialMarmi, OuTeX TC_00013, XU HR, ALOT and STex validate the efficiency of the proposed method. The proposed Hess-ACS-LBP method achieves about 20% improvement over the original LBP method and 1%-11% improvement over the other state-of-the-art hand-crafted LBP methods in terms of classification accuracy. Besides, the experimental results show that the proposed method achieves up to 32% better results than the state-of-the-art deep learning based methods. Especially, the performance of the proposed method on ALOT and STex datasets containing many classes is remarkable. INDEX TERMS Hessian matrix, feature extraction, local binary patterns, texture classification.

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Section: Previous Workmentioning

confidence: 99%

Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification

Alpaslan

Hanbay

2020

IEEE Access

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“…This success is mainly owing to two merits of the method: the first one is efficiency, which makes it very popular for the community of image analysis. The second one is flexibility as it can be adapted easily to tackle the requirements of various kinds of applications, even for non-traditional texture [54], LETRIST [48], local concave-and-convex micro structure patterns (LCCMSP) [55], MNTCDP [46], local optimaloriented pattern (LOOP) [56], local directional decoded ternary pattern [57], attractive-and-repulsive centre-symmetric LBP (ARCS-LBP) [58] etc. These LBP variants are mostly based on the improvement of one or more of the following original LBP elements:…”

Section: List Of Evaluated Texture Descriptorsmentioning

confidence: 99%

“…Note that LETRIST, BSIF, and LDN descriptors are in the top five descriptors on four tested datasets and MNTCDP and SLGS descriptors are in the top five descriptors on three tested datasets. AHP, which is the top-performing descriptor on the [55,57], which is performed on all the pairwise combinations of the 50 local texture descriptors on the five tested databases. Our goal here is to give an overall ranking of the different tested descriptors over the used palmprint datasets.…”

Section: 21mentioning

confidence: 99%

Palmprint recognition using state‐of‐the‐art local texture descriptors: a comparative study

2020

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Several human being traits can be used as a robust and distinctive identifier for a given person. The palm region of the hand is one of these features that researchers in biometric fields have given a huge consideration in recent years. Many works have been proposed in the literature to design palmprint (an image acquired of the palm region) recognition framework. Extraction of prominent image local features is a critical module in most of these approaches. Local Binary Patterns (LBP) like methods, have emerged as one of the most effective feature extraction techniques. Despite a period of remarkable evolution, neither extensive and comprehensive evaluation nor comparison has been performed to date on a large number of LBP variants and non-LBP texture methods in palmprint recognition problem. Motivated by this, this paper aims to fill that gap and provide a comprehensive comparative study of the performance of a large number of recent texture descriptors in palmprint recognition. Extensive experimental results on the well-known constrained and unconstrained challenging palmprint databases, indicate that a number of tested local texture descriptors, which are evaluated for the first time on palmprint recognition, achieve promising results. Classification results are statistically compared through Wilcoxon signed rank test.

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“…CSQP computes an eight-bit pattern from 16 pixels in the local neighborhood. Issam et al [57] proposed local directional ternary pattern (LDTP) for texture classification. LDTP, which exploits both LDP's and LTP's concepts, encodes both contrast information and directional pattern features in a compact way based on local derivative variations.…”

Section: Introductionmentioning

confidence: 99%

Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

et al. 2020

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This paper ideates a new computationally simple and effective local image feature descriptor, referred to as Directional Neighborhood Topologies based Multi-scale Quinary Pattern (DNT-MQP) for texture description and classification. The essence of DNT-MQP is to encode the structure of local neighborhood by analyzing the differential excitation and directional information using various directional neighborhood topologies and new pattern encoding scheme. We first designed four different versions of single scale DNT-QP features based on four directional neighborhood topologies based sampling sets which are then combined together to build the effective multiscale DNT-MQP model. Unlike some existing parametric methods that employ static thresholds to perform thresholding, the construction process of DNT-MQP includes an automatic mechanism for dynamic thresholds estimation. Thanks to a richer local description ensured by exploiting complementary information resulting from single scale DNT-QP operators' combination, DNT-MQP descriptor has high capability to elicit stable and discriminative feature representation than other local feature descriptors. In addition, DNT-QP has the advantages of computational simplicity in feature extraction and low-dimensionality in feature representation. The effectiveness of DNT-QP is evaluated on sixteen challenging texture datasets and it is found that it maintains a high level of performance stability where the achieved performances are competitive or better than several recent most promising state-of-theart texture descriptors as well as deep learning-based feature extraction approaches. Impressively, DNT-MQP showed good tolerance to rotation as well as illumination, scale and viewpoint changes against certain descriptors which are originally conceived to deal with these challenges. Furthermore, statistical hypothesis testing through Wilcoxon signed rank test is applied to prove the statistical significance of the accuracy improvement obtained in all the datasets.

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Local directional ternary pattern: A New texture descriptor for texture classification

Cited by 79 publications

References 50 publications

Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification

Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification

Palmprint recognition using state‐of‐the‐art local texture descriptors: a comparative study

Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

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