Feature Extraction Using Block-based Local Binary Pattern for Face Recognition

Moujahid, Abdelmalik; Abanda, Amaia; Dornaika, Fadi

doi:10.2352/issn.2470-1173.2016.10.robvis-394

Cited by 5 publications

(3 citation statements)

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“…The selection is based on the histogram score evaluated for each of the

available histograms. In previous works, several scores were proposed in the literature to evaluate the relevance of histograms: the Intra-Class Similarity score (ICS-score), proposed by Porebski et al [ 17 ], which is based on an intra-class similarity measure; the Adapted Supervised Laplacian score (ASL-score), proposed by Kalakech et al [ 18 ], which evaluates the relevance of histograms using the local properties of the image data; the Simba-2 score, proposed by Mouhajid et al [ 20 ], which is based on the hypothesis margin and the

distance; and the Sparse Adapted Supervised Laplacian score (SpASL-score) [ 21 ]. Only the two first scores were applied in a multi color space framework.…”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

“…It fundamentally differs from all the previous approaches which select the bins of the LBP histograms or project them into a discriminant space. Several scores were proposed in the literature to evaluate the relevance of histograms: the Intra-Class Similarity score (ICS-score), proposed by Porebski et al [ 17 ], which is based on an intra-class similarity measure; the Adapted Supervised Laplacian score (ASL-score) and Adapted Laplacian score (AL-score), proposed by Kalakech et al [ 18 , 19 ], which evaluates the relevance of the histograms using the local properties of the image data; the Simba-2 score, proposed by Mouhajid et al [ 20 ], which is based on the hypothesis margin and the

distance; and the Sparse Adapted Supervised Laplacian score (SpASL-score) [ 21 ], which is based on the ASL-score and a sparse representation. The LBP histogram selection approach using ICS or ASL scores was recently extended to the multi color space domain and showed its relevance compared to the bin selection approach proposed by Guo [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

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“…The selection is based on the histogram score evaluated for each of the

distance; and the Sparse Adapted Supervised Laplacian score (SpASL-score) [ 21 ]. Only the two first scores were applied in a multi color space framework.…”

Section: Sparse-mcshs and Sparse-mcsbs Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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“…The Laplacian score is used for feature selection for histogram bins as features. In another approach, feature selection used a block-based Local Binary Pattern for face recognition [55] In this paper, a score-based feature selection method is proposed. The main goal of this paper is related to proposed a framework to achieve high classification rate only by very small number of features.…”

Section: Introductionmentioning

confidence: 99%

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

Gemeay,

Alenizi,

Mohammed

et al. 2023

IEEE Access

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There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptor for revealing the underlying structure of a texture. Recently several variants of local binary descriptor have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently providing a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of descriptor.

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Feature selection and mapping of local binary pattern for texture classification

Shakoor

Boostani

Sabeti

et al. 2022

Multimed Tools Appl

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Feature Extraction Using Block-based Local Binary Pattern for Face Recognition

Cited by 5 publications

References 0 publications

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

Feature selection and mapping of local binary pattern for texture classification

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