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Average Analysis Method in Selecting Haralick’s Texture Features on Color Co-occurrence Matrix for Texture Based Image Retrieval

첫 페이지 보기
  • 발행기관
    보안공학연구지원센터(IJMUE) 바로가기
  • 간행물
    International Journal of Multimedia and Ubiquitous Engineering SCOPUS 바로가기
  • 통권
    Vol.11 No.2 (2016.02)바로가기
  • 페이지
    pp.79-88
  • 저자
    Abd Rasid Mamat, Mohd Khalid Awang, Norkhairani Abdul Rawi, Mohd. Isa Awang, Mohd Fadzil Abdul Kadir
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A268439

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원문정보

초록

영어
Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Methodology
  3.1 Generated Color Co-Occurrence Matrix (CCM)
  3.2 Extraction the Haralick Textures on CCM
  3.3 Feature Selection
  3.4 Principal Component Analysis
  3.5 Average Analysis
  3.6 Similarity Measure and Performance Evaluation
 4. Experiment Setup
 5. Results and Discussion
 6. Conclusion and Future Work
 References

키워드

Texture based image retrieval Color Co-occurrence matrix Haralick’s texture features Average Analysis Principal Component Analysis

저자

  • Abd Rasid Mamat [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia ]
  • Mohd Khalid Awang [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia ]
  • Norkhairani Abdul Rawi [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia ]
  • Mohd. Isa Awang [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia ]
  • Mohd Fadzil Abdul Kadir [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
  • 설립연도
    2006
  • 분야
    공학>컴퓨터학
  • 소개
    1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업

간행물

  • 간행물명
    International Journal of Multimedia and Ubiquitous Engineering
  • 간기
    월간
  • pISSN
    1975-0080
  • 수록기간
    2008~2016
  • 등재여부
    SCOPUS
  • 십진분류
    KDC 505 DDC 605

이 권호 내 다른 논문 / International Journal of Multimedia and Ubiquitous Engineering Vol.11 No.2

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