NDLI: Dominant Local Binary Patterns for Texture Classification

Content Provider	IEEE Xplore Digital Library
Author	Liao, S. Law, M.W.K. Chung, A.C.S.
Copyright Year	1992
Abstract	This paper proposes a novel approach to extract image features for texture classification. The proposed features are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local binary patterns (DLBP) in a texture image and the supplementary features extracted by using the circularly symmetric Gabor filter responses. The dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information, while the Gabor-based features aim at supplying additional global textural information to the DLBP features. Through experiments, the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram-equalized, randomly rotated and noise corrupted images in Outex, Brodatz, Meastex, and CUReT texture image databases. Our method has also been compared with six published texture features in the experiments. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions.
Sponsorship	IEEE Signal Processing Society
Page Count	12
File Size	1642685
Starting Page	1107
Ending Page	1118
File Format	PDF
ISSN	10577149
Volume Number	18
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gabor filters Histograms Feature extraction Data mining Image databases Biomedical imaging Image texture analysis Markov random fields Lighting Noise robustness texture classification Circularly symmetric Gabor filter local binary pattern rotation invariance
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Completed Local Binary Count for Rotation Invariant Texture Classification

Local binary patterns partitioning for rotation invariant texture classification

BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns

Rotation Invariant Local Frequency Descriptors for Texture Classification

Rotation-Invariant Image and Video Description With Local Binary Pattern Features

Texture Classification Using Dominant Neighborhood Structure

A Completed Modeling of Local Binary Pattern Operator for Texture Classification

BRINT: A binary rotation invariant and noise tolerant texture descriptor

Dominant Local Binary Patterns for Texture Classification

Similar Documents

Completed Local Binary Count for Rotation Invariant Texture Classification

Local binary patterns partitioning for rotation invariant texture classification

BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns

Rotation Invariant Local Frequency Descriptors for Texture Classification

Rotation-Invariant Image and Video Description With Local Binary Pattern Features

Texture Classification Using Dominant Neighborhood Structure

A Completed Modeling of Local Binary Pattern Operator for Texture Classification

BRINT: A binary rotation invariant and noise tolerant texture descriptor

Dominant Local Binary Patterns for Texture Classification