A feature level fusion in similarity matching to content-based image retrieval

2006 9th International Conference on Information Fusion, FUSION

Volumn , Issue , 2006, Pages

  Rahman, Md Mahmudur ^a   Desai, Bipin C ^a   Bhattacharya, Prabir ^b  

^a Dept of Electrical and Computer Engineering   (Canada)

^b NONE   (Canada)

Author keywords

Clustering; Feature extraction; Image retrieval; Segmentation; Similarity measure

Indexed keywords

AGGLOMERATION; COLOR; CONTENT BASED RETRIEVAL; DATA FUSION; FEATURE EXTRACTION; FUSION REACTIONS; IMAGE RETRIEVAL; INFORMATION FUSION; INFORMATION RETRIEVAL; NUCLEAR PHYSICS; OPTICAL PROPERTIES; SENSOR DATA FUSION;

CLUSTERING; CONTENT-BASED IMAGE RETRIEVAL; DESCRIPTORS; EDGE HISTOGRAM DESCRIPTORS; FEATURE-LEVEL FUSION; FLEXIBLE TOOLS; IMAGE DATABASES; IMAGE REPRESENTATIONS; IMAGE SIMILARITIES; INTERNATIONAL CONFERENCES; LEVELS OF ABSTRACTION; LOCAL COLOR; LOCAL REGIONS; RETRIEVAL EFFECTIVENESS; SEGMENTATION; SEGMENTED REGIONS; SIMILARITY CALCULATIONS; SIMILARITY MATCHING; SIMILARITY MEASURE; SUB-IMAGES; TEXTURE DESCRIPTORS;

IMAGE FUSION;

EID: 50149116187     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2006.301664     Document Type: Conference Paper

References (13)

1. A. Smeulder, M. Worring, S. Santini, A. Gupta, R. Jain, Content-Based Image Retrieval at the End of the Early Years., IEEE Trans. on Pattern Anal, and Machine Intell., 22:1349-1380, 2000.
2. Y. Rui, T. S. Huang, and S.-F. Chang, Image Retrieval: Current Techniques, Promising Directions and Open Issues. Journal of Visual Communication and Image Representation, 10(4): 39-62, 1999.
3. C. Carson, S. Belongie, H. Greenspan, and J. Malik, Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying, IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(8): 1026-1038, 2002.
4. S. Ardizzoni, I. Bartolini, and M. Patella, Wind-surf: Region-based image retrieval using wavelets, in DEXA Workshop, 167-173, 1999.
5. J. Z. Wang, J. Li, and G. Wiederhold, SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries, IEEE Trans. on Pattern Analysis and Machine. Intelligence, 23(9): 947-963, 2001.
6. W. Y. Ma, B. Manjunath. Na Tra: A toolbox for navigating large image databases. Proc. IEEE Int. Conf. Image Processing, pp. 568-571, Santa Barbara, 1997.
7. J. Canny, A computational approach to edge detection., IEEE Trans. Pattern Anal. Machine Intell., 8: 679-698, 1986.
8. A. K. Jain, B. Bhandrasekaran, Dimensionality and sample size considerations in pattern recognition practice., Handbook of Statistics, 2: 835-855, 1987.
9. M. Strieker, M. Orengo, Similarity of color images: Storage and Retrieval for Image and Video Databases., SPIE, vol. 2420, 1995.
10. S. Aksoy, R. M. Haralick, Texture Analysis in Machine Vision., Chapter Using Texture in Image Similarity and Retrieval, Series on Machine Perception and Artificial Intelligence., World Scientific, 2000.
11. M. M. Rahman , P. Bhattacharya and B.C. Desai, Similarity Searching in Image Retrieval with Statistical Distance Measure and Supervised Learning, International Conference on Advances in Pattern Recognition (ICAPR), LNCS, 3686 : 315-324, 2005.
12. K. Fukunaga, Introduction to Statistical Pattern Recognition, Second ed., Academic Press, 1990.
13. C. Leung, H. Ip, Benchmarking for Content Based Visual Information Search, Fourth International Conference on Visual Information Systems (VISUAL2000), LNCS, 1929:442-456, 2000.