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Research on New Multi-Feature Large-Scale Image Retrieval Algorithm based on Semantic Parsing and Modified Kernel Clustering Method
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- 발행기관
- 보안공학연구지원센터(IJSIA) 바로가기
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- 간행물
- International Journal of Security and Its Applications SCOPUS 바로가기
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- 통권
- Vol.10 No.1 (2016.01)바로가기
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- 페이지
- pp.139-154
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A269887
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Because of the feature points can describe the local characteristics of the image in a reasonable manner, effective use of feature point of content based image retrieval become the current hot issues in the field of computer vision. Aiming at this problem, we put forward a kind of combination clustering based on feature points, a new method of image retrieval. The method includes the combination of feature point clustering algorithm and based on the algorithm of local color histogram construction strategy. With the existing and local color histogram retrieval method based on feature points, compared to the method can effectively solve the current method of feature point location information and feature point center relying too much on the problem. Subjectivity and as a result of the manual annotation image accuracy, the traditional image retrieval methods cannot meet the needs of the user. Multidimensional indexing technology is only from the perspective of how to improve the indexing algorithm to adapt to the large-scale database to consider a problem, in content-based image retrieval. Our research combines the advantages of the semantic analysis and kernel clustering which will enhance the performance of the traditional image retrieval methods and strengthen the feasibility of the algorithm.
목차
Abstract
1. Introduction
2. The Basic Knowledge on Clustering and Semantic Analysis
2.1. The Kernel Clustering Algorithms for Image Retrieval
2.2. The Semantic Analysis for the Image Retrieval
3. The Proposed Algorithm
3.1. The Multi-Feature Large-Scale Image Characteristics
3.2. The Semantic Parsing and Kernel Clustering based Algorithm
4. The Experiment Analysis
4.1. The Set-up of the Experiment
4.2. The Statistical Result of the Experiment
5. Conclusion and Summary
References
키워드
Image Retrieval Semantic Parsing Kernel Clustering Large-Scale Feature Extraction Multi-Feature Randomly Scattered저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSIA) [Science & Engineering Research Support Center, Republic of Korea(IJSIA)]
- 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
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- 간행물명
- International Journal of Security and Its Applications
- 간기
- 격월간
- pISSN
- 1738-9976
- 수록기간
- 2008~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
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