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Texture Classification Using Dense Micro-block Difference (DMD)

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationCOMPUTER VISION - ACCV 2014, PT II
EditorsD Cremers, Reid, H Saito, MH Yang
PublisherSpringer-Verlag, Berlin
Pages643-658
Number of pages16
ISBN (Print)978-3-319-16807-4
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventAsian Conference on Computer Vision -
Duration: 1 Jan 1900 → …

Publication series

NameLecture Notes in Computer Science
PublisherSPRINGER-VERLAG BERLIN
Volume9004
ISSN (Print)0302-9743

Conference

ConferenceAsian Conference on Computer Vision
Period1/01/00 → …

Abstract

The paper proposes a novel image representation for texture classification. The recent advancements in the field of patch based features compressive sensing and feature encoding are combined to design a robust image descriptor. In our approach, we first propose the local features, Dense Micro-block Difference (DMD), which capture the local structure from the image patches at high scales. Instead of the pixel we process the small blocks from images which capture the micro-structure from it. DMD can be computed efficiently using integral images. The features are then encoded using Fisher Vector method to obtain an image descriptor which considers the higher order statistics. The proposed image representation is combined with linear SVM classifier. The experiments are conducted on the standard texture datasets (KTH-TIPS-2a, Brodatz and Curet). On KTH-TIPS-2a dataset the proposed method outperforms the best reported results by 5.5% and has a comparable performance to the state-of-the-art methods on the other datasets.

Keywords

  • LOCAL BINARY PATTERNS, IMAGE CLASSIFICATION, FEATURES, MODELS

Publication forum classification

Field of science, Statistics Finland