A relevance feedback retrieval method based on Tamura texture

2009 2nd International Symposium on Knowledge Acquisition and Modeling, KAM 2009

Volumn 3, Issue , 2009, Pages 174-177

  Qi, Ya Li ^a  

^a BEIJING INSTITUTE OF GRAPHIC COMMUNICATION   (China)

Author keywords

Content based image retrieval; Support vector machines; Tamura texture

Indexed keywords

CONTENT-BASED IMAGE RETRIEVAL; HUMAN VISION; HYPER-PLANE; LOW-LEVEL FEATURES; RELEVANCE FEEDBACK METHOD; RELEVANCE FEEDBACK RETRIEVAL; RETRIEVAL STRATEGIES; SEMANTIC GAP; TEXTURE FEATURES;

FEEDBACK; INFORMATION RETRIEVAL; KNOWLEDGE ACQUISITION; SEMANTICS; SUPPORT VECTOR MACHINES; TEXTURES;

CONTENT BASED RETRIEVAL;

EID: 77950986176     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/KAM.2009.39     Document Type: Conference Paper

References (7)

1. O.L. Mangasarian, Generalized support vector machines, Advances in Large Margin Classifiers, Cambridge, MA, 2000, pp: 135-146.
2. T Deselaers, D Keysers, H Ney. Features for image retrieval: A quantitative comparison. Lecture Notes in Computer Science, Springer, 2004, Volume 3175/2004, pp.228-236.
3. H. Tamura, S. Mori, T. Yamawaki.Textural Features Corresponding to Visual Perception.IEEE Transaction on Systems, Man, and Cybernetcs, 1978, Vol.SMC-8, No.6, pp.460-472.
4. Li Qing, Yong, HU Hong, SHi ZhiPing, SHi ZhongZhi, Research on Texture-Based Semantic Image Retrieval, Chinese Journal of Computers, Vol.29, No.1, 2006, pp.116-122.
5. J.C. Platt, Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods:Support Vector Learning. MIT Press, 1999.
6. Amoid W.M., Marce W., Simone S.et al.Content based image retrieval at the end of the early years.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12).pp:1349-1379.
7. Karkanis S, Galousi K, Maroulis D.Classification of endoscopic images based on texture spectrum.In:Workshop on Machine Learning in Medical Applications.Chania, 1999., pp:63-69.