Texture image retrieval using local binary edge patterns

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Texture is a fundamental property of surfaces, and as so, it plays an important role in the human visual system for analysis and recognition of images. A large number of techniques for retrieving and classifying image textures have been proposed during the last few decades. This paper describes a new texture retrieval method that uses the spatial distribution of edge points as the main discriminating feature. The proposed method consists of three main steps: First, the edge points in the image are identified; then the local distribution of the edge points is described using an LBP-like coding. The output of this step is a 2D array of LBP-like codes, called LBEP image. The final step consists of calculating two histograms from the resulting LBEP image. These histograms constitute the feature vectors that characterize the texture. The results of the experiments that have been conducted show that the proposed method significantly improves the traditional edge histogram method and outperforms several other state-of-the art methods in terms of retrieval accuracy.

Original languageEnglish
Title of host publicationDigital Information and Communication Technology and Its Applications - International Conference, DICTAP 2011, Proceedings
Pages219-230
Number of pages12
Volume166 CCIS
EditionPART 1
DOIs
Publication statusPublished - 2011
EventInternational Conference on Digital Information and Communication Technology and Its Applications, DICTAP 2011 - Dijon, France
Duration: Jun 21 2011Jun 23 2011

Publication series

NameCommunications in Computer and Information Science
NumberPART 1
Volume166 CCIS
ISSN (Print)1865-0929

Other

OtherInternational Conference on Digital Information and Communication Technology and Its Applications, DICTAP 2011
CountryFrance
CityDijon
Period6/21/116/23/11

Fingerprint

Image retrieval
Textures
Image texture
Spatial distribution
Experiments

Keywords

  • Edge detection
  • Local Binary Edge Patterns
  • Texture-based Image Retrieval

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Abdesselam, A. (2011). Texture image retrieval using local binary edge patterns. In Digital Information and Communication Technology and Its Applications - International Conference, DICTAP 2011, Proceedings (PART 1 ed., Vol. 166 CCIS, pp. 219-230). (Communications in Computer and Information Science; Vol. 166 CCIS, No. PART 1). https://doi.org/10.1007/978-3-642-21984-9_19

Texture image retrieval using local binary edge patterns. / Abdesselam, Abdelhamid.

Digital Information and Communication Technology and Its Applications - International Conference, DICTAP 2011, Proceedings. Vol. 166 CCIS PART 1. ed. 2011. p. 219-230 (Communications in Computer and Information Science; Vol. 166 CCIS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdesselam, A 2011, Texture image retrieval using local binary edge patterns. in Digital Information and Communication Technology and Its Applications - International Conference, DICTAP 2011, Proceedings. PART 1 edn, vol. 166 CCIS, Communications in Computer and Information Science, no. PART 1, vol. 166 CCIS, pp. 219-230, International Conference on Digital Information and Communication Technology and Its Applications, DICTAP 2011, Dijon, France, 6/21/11. https://doi.org/10.1007/978-3-642-21984-9_19
Abdesselam A. Texture image retrieval using local binary edge patterns. In Digital Information and Communication Technology and Its Applications - International Conference, DICTAP 2011, Proceedings. PART 1 ed. Vol. 166 CCIS. 2011. p. 219-230. (Communications in Computer and Information Science; PART 1). https://doi.org/10.1007/978-3-642-21984-9_19
Abdesselam, Abdelhamid. / Texture image retrieval using local binary edge patterns. Digital Information and Communication Technology and Its Applications - International Conference, DICTAP 2011, Proceedings. Vol. 166 CCIS PART 1. ed. 2011. pp. 219-230 (Communications in Computer and Information Science; PART 1).
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