Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition

Lee, Seol‐Hoon; Choi, Jae Young; Ro, Yong Man; Plataniotis, Konstantinos N.

doi:10.1109/tip.2011.2181526

Cited by 128 publications

(85 citation statements)

References 24 publications

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“…Local derivative pattern [13] was proposed for face recognition under challenging image conditions. A novel face descriptor named local color vector binary pattern (LCVBP) [7] was proposed to recognize face images with challenges. Two color local texture features like color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP) [4] were purposed for face recognition and both were combined to maximize their complementary effect of color and texture information respectively.…”

Section: Motivation and Justification Of The Proposed Approachmentioning

confidence: 99%

A textural approach for land cover classification of remotely sensed image

Jenicka

Suruliandi

2014

CSIT

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Texture features play a vital role in land cover classification of remotely sensed images. Local binary pattern (LBP) is a texture model that has been widely used in many applications. Many variants of LBP have also been proposed. Most of these texture models use only two or three discrete output levels for pattern characterization. In the case of remotely sensed images, texture models should be capable of capturing and discriminating even minute pattern differences. So a multivariate texture model is proposed with four discrete output levels for effective classification of land covers. Remotely sensed images have fuzzy land covers and boundaries. Support vector machine is highly suitable for classification of remotely sensed images due to its inherent fuzziness. It can be used for accurate classification of pixels falling on the fuzzy boundary of separation of classes. In this work, texture features are extracted using the proposed multivariate descriptor, MDLTP/MVAR that uses multivariate discrete local texture pattern (MDLTP) supplemented with multivariate variance (MVAR). The classification accuracy of the classified image obtained is found to be 93.46 %.

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Section: Motivation and Justification Of The Proposed Approachmentioning

confidence: 99%

A textural approach for land cover classification of remotely sensed image

Jenicka

Suruliandi

2014

CSIT

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“…In local quantized patterns (LQP) [33], a clustering method was adopted. The local color vector binary pattern (LCVBP) [34] is especially developed for color image applications. A texture classification method named local vector quantization pattern [LQVP] [35] developed recently aimed at quantizing the whole difference vector between the central pixel and its neighborhood pixels instead of each neighborhood pixels separately and it attained a good classification result on UIUC, Outex and Brodatz data bases.…”

Section: Introductionmentioning

confidence: 99%

Texture Classification based on Local Features Using Dual Neighborhood Approach

Rao¹,

Kumar²,

Prasad³

2017

IJIGSP

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Abstract-Texture classification and analysis are the most significant research topics in computer vision. Local binary pattern (LBP) derives distinctive features of textures. The robustness of LBP against gray-scale and monotonic variations and computational advantage have made it popular in various texture analysis applications. The histogram techniques based on LBP is complex task. Later uniform local binary pattern's (ULBP) are derived on LBP based on bit wise transitions. The ULBP's are rotationally invariant. The ULBP approach treated all non-uniform local binary pattern's (NULBP) into one miscellaneous label. This paper presents a new texture classification method incorporating the properties of ULBP and grey-level co-occurrence matrix (GLCM). This paper derives ternary patterns on the ULBP and divides the 3 x 3 neighborhood in to dual neighborhood. The ternary pattern mitigates the noise problems particularly near uniform regions. The dual neighborhood reduces the range of texture unit from 0 to 6561 to 0 to 80. The GLCM features extracted from ULBP-dual texture matrix (ULBP-DTM) provide complete texture information about the image and reduce the texture unit range. Various machine learning classifiers are used for classification purpose. The performance of the proposed method is tested on Brodtaz, Outex and UIUC's textures and compared with GLCM, texture spectrum (TS) and cross-diagonal texture matrix (CDTM) approaches.

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“…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.…”

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confidence: 99%

Texture Classification based on First Order Local Ternary Direction Patterns

Rao¹,

Kumar²,

Prasad³

2017

IJIGSP

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Abstract-The local binary pattern (LBP) and local ternary pattern (LTP) are basically gray scale invariant, and they encode the binary/ ternary relationship between the neighboring pixels and central pixel based on their grey level differences and derives a unique code. These traditional local patterns ignore the directional information. The proposed method encodes the relationship between the central pixel and two of its neighboring pixel located in different angles (α, β) with different directions. To estimate the directional patterns, the present paper derived variation in local direction patterns in between the two derivates of first order and derived a unique First order -Local Direction variation pattern (FO-LDVP)

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Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition

Cited by 128 publications

References 24 publications

A textural approach for land cover classification of remotely sensed image

A textural approach for land cover classification of remotely sensed image

Texture Classification based on Local Features Using Dual Neighborhood Approach

Texture Classification based on First Order Local Ternary Direction Patterns

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