Grayscale-Inversion and Rotation Invariant Texture Description Using Sorted Local Gradient Pattern

Song, Tianyou; Xin, Liangliang; Gao, Chenqiang; Zhang, Gang; Zhang, Tianqi

doi:10.1109/lsp.2018.2809607

Cited by 44 publications

(26 citation statements)

References 31 publications

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“…For CPS strategy, it was fused with ELDP (called CPS_ELDP in the paper). We test the approaches on four face databases, ORL [40], CALTECH [41], GEORGIA [42], and FACE94 [44] in the experiment, and (R l , 8t) was set (1,8), (2,16), (3,24), (4,32), (5,32), (6,32) and (7,32), respectively. The Nearest Neighbor Classifier (NNC) was chosen for face recognition.…”

Section: A Experimental Set-upmentioning

confidence: 99%

“…In addition, it combines structural as well as the statistical features. Because of the advantages of LBP, numerous improved operators have been introduced in recent years [13]- [16], [48], [49]. In order to achieve multi-resolution analysis, Ojala et al [17] extended LBP from 8-neighborhood to arbitrary circular neighborhoods (R, P), where P and R represent the number of sampling points and the radius respectively.…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Sun

Tang

et al. 2020

IEEE Access

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Face recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gray transitions in the local neighborhood is used to form the LGNP code based on the LDP-based methods. Then, the concept of fuzzy convex-concave partition (FCCP) is introduced to fuse the global and regional information based on convex-concave partition (CCP). By the combination of LGNP and FCCP, the proposed descriptor is denoted as FCCP_LGNP. To evaluate the performance of FCCP_LGNP comprehensively, a series of experiments were carried out on four different face databases ORL, CALTECH, GEORGIA, and FACE94, and the results show that FCCP_LGNP is superior to the recent state-of-the-art methods based on hand-crafted features. Even compared with the deep learning methods, VGG16 and ResNet101, the proposed descriptor still shows good performance. INDEX TERMS Face recognition, local gradient number pattern, fuzzy convex-concave partition, LDP-based methods.

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Section: A Experimental Set-upmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Sun

Tang

et al. 2020

IEEE Access

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“…Similar to the TCC-Census and 3M-Census that use multilevel encoding function, local ternary pattern (LTP) [23] and elongated quinary pattern (EQP) [24] were proposed. There were also attempts to change the shape or size of the patch [25], [26]. Similar to the RG-Census, there were several LBP-based methods that perform the domain transformation before encoding the binary code stream [26]- [28].…”

Section: Local Binary Patterns and Its Variantsmentioning

confidence: 99%

“…There were also attempts to change the shape or size of the patch [25], [26]. Similar to the RG-Census, there were several LBP-based methods that perform the domain transformation before encoding the binary code stream [26]- [28]. However, owing to the difference between stereo matching and texture classification, these methods [26]- [28] adopt the domain transformation to make the image descriptor rotationinvariant, which is not desired for stereo matching.…”

Section: Local Binary Patterns and Its Variantsmentioning

confidence: 99%

Quaternary Census Transform Based on the Human Visual System for Stereo Matching

Kim

Lim

et al. 2020

IEEE Access

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The census transform is a non-parametric local transform that is widely used in stereo matching. This transform encodes the structural information of a local patch into a binary code stream representing the relative intensity ordering of the pixels within the patch. Despite its high performance in stereo matching, the census transform often generates identical binary code streams for two different patches because it simply thresholds the pixels within the patch at the center pixel intensity. To overcome this problem, we introduce a quaternary census transform that encodes the local structural information into a quaternary code stream by employing both the relative intensity ordering and the minimum visibility threshold of the human eye known as the just-noticeable difference. Moreover, because the human eye activates different areas of the retina based on brightness, the patch size for the proposed quaternary census transform adaptively varies depending on the luminance of each pixel. Experimental results on well-known Middlebury stereo datasets prove that the proposed transform outperforms the other census transform-based methods in terms of the accuracy of stereo matching. INDEX TERMS Census transform, depth estimation, disparity map, human visual system, similarity cost calculation, stereo image processing, stereo matching.

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“…2) The image gradient feature descriptor is first introduced to cluster large scale face datasets.A name of sorted local gradient pattern is grayscale inversion and rotation invariant descriptors for texture classification. Image rotation and linear or non-linear grayscale-inversion changes are highly discriminative and robust [19].Different feature descriptors are compared for face clustering. Experimental results show that image gradient feature descriptor is very simple but very competitive compared with other feature descriptors, e.g., HOG, LBP and Gabor.Another novel color image inpainting algorithm.…”

Section: Introductionmentioning

confidence: 99%

Face Clustering Utilizing Scalable Sparse Subspace Clustering And The Image Gradient Feature Descriptor

Liu¹,

Li²,

Sun³

et al. 2018

EasyChair Preprints

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Face clustering is an important topic in  computer vision. It aims to put together facial images that belong to the same person. Spectral clustering-based algorithms are often used for accurate face clustering. However, a big occlusion matrix is usually needed to deal with the noise and sparse outlier terms, which makes the sparse coding process computationally expensive. Thus spectral clustering-based algorithms are difficult to extend to large scale datasets. In this paper, we use the image gradient feature descriptor and scalable Sparse Subspace Clustering algorithm for large scale and high accuracy face clustering. Within the image gradient feature descriptor, the scalable Sparse Subspace Clustering algorithm can be used in large scale face datasets without sacrificing clustering performance. Experimental results show that our algorithm is robust to illumination, occlusion, and achieves a relatively high clustering accuracy on the Extended Yale B and AR datasets.

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Grayscale-Inversion and Rotation Invariant Texture Description Using Sorted Local Gradient Pattern

Cited by 44 publications

References 31 publications

Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Quaternary Census Transform Based on the Human Visual System for Stereo Matching

Face Clustering Utilizing Scalable Sparse Subspace Clustering And The Image Gradient Feature Descriptor

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