원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.3
2016.03
pp.291-300
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The image retrieval performance based on single feature is limited. For different kinds of images, it can not a better retrieval result. This paper raises image retrieval method based on weighted multi feature. In each kind of images, each feature precision is the weight evidence. On this basis, we research the existing semantic retrieval technology. Choosing the SVM classification theory which is more mature. Selecting parts of images as training set. Doing the training classification. From the research of different characteristics priority, it raises the image retrieval technology which synthesizes SVM and multi feature. From that, it can get a higher retrieval efficiency.
목차
Abstract
1. Introduction
2. Basic Principle of SVM
2.1 VC Dimension Theory
2.2 Minimization Principle of Structure Risk
3. SVM Classification
3.1 SVM Dichotomies
3.2 SVM Multi-Classification
4. Image Retrieval Experiments and Performance Analysis based on SVM Semantic Classification
4.1 SVM Training Algorithm
4.2 Experimental Procedure
4.3 Experimental Results and Analysis
5. Conclusion
References
1. Introduction
2. Basic Principle of SVM
2.1 VC Dimension Theory
2.2 Minimization Principle of Structure Risk
3. SVM Classification
3.1 SVM Dichotomies
3.2 SVM Multi-Classification
4. Image Retrieval Experiments and Performance Analysis based on SVM Semantic Classification
4.1 SVM Training Algorithm
4.2 Experimental Procedure
4.3 Experimental Results and Analysis
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보