Fast search in large-scale image database using vector quantization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2728, Issue , 2003, Pages 477-487

  Ye, Hangjun ^a   Xu, Guangyou ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT BASED RETRIEVAL; DATA HANDLING; DATABASE SYSTEMS; IMAGE RETRIEVAL; INDEXING (OF INFORMATION); MAXIMUM PRINCIPLE; SEARCH ENGINES; VECTOR SPACES; VECTORS;

CONTENT BASED IMAGE RETRIEVAL SYSTEMS; CURSE OF DIMENSIONALITY; DATA PARTITIONING; EXACT SIMILARITY SEARCHES; EXPECTATION-MAXIMIZATION METHOD; GAUSSIAN MIXTURE DISTRIBUTION; HIGH DIMENSIONALITY; RETRIEVAL EFFICIENCY;

VECTOR QUANTIZATION;

EID: 35248875315     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45113-7_47     Document Type: Article

References (18)

1. Y. Rui, T. Huang, S. Chang: Image Retrieval: Current Techniques, Promising Directions and Open Issues. J. of Visual Communication and Image Representation, vol. 10, (1999) 1-23
2. Y. Rui, T. S. Huang, M. Ortega, et al.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. on CSVT, no. 5, (1998) 644-655
3. J. Nievergelt, H. Hinterberger, K. Sevcik: The gridfile: An adaptable symmetric multikey file structure. ACM Transactions on Database Systems, vol. 9, no. 1, (1984) 38-71
4. J. Robinson: The k-d-b-tree: A search structure for large multidimensional dynamic indexes. Proc. of the ACM SIGMODICMD (1981) 10-18
5. N. Beckmann, H. P. Kriegel, R. Schneider, et al.: The R*-tree: An efficient and robust access method for points and rectangles. Proc. ACM SIGMOD ICMD (1990) 322-331
6. N. Katayama, S. Satoh: The SR-tree: An index structure for high-dimensional nearest neighbor queries. Proc. ACM SIGMOD Int. Conf. Management of Data (1997) 369-380
7. R. Weber, H. Schek, S. Blott: A quantitative analysis and performance study for simi-larity-search methods in high-dimensional spaces. Proc. ACM VLDB (1998)
8. K. Beyer, J. Goldstein, R. Ramakrishnan: When Is 'Nearest Neighbor' Meaningful?. Proc. of the 7th International Conference on Database Theory, Jerusalem (1999) 217-235
9. D. W. Scott, Density Estimation, Wiley, New York (1992)
10. H. Ferhatosmanoglu, E. Tuncel, D. Agrawal: Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets. ACM CKIM, McLean, (2000)
11. P. Wu, B. Manjunath, S. Chandrasekaran: An adaptive index structure for high-dimensional similarity search. Proc. PCM, Beijing, China, (2001) 71-77
12. G.-H. Cha, X. Zhu, D. Petkovic, et al: An efficient indexing method for nearest neighbor searches in high-dimensional image databases. IEEE Trans. Multimedia, vol. 4, no. 1, (2002) 76-87
13. A. Gersho, R. M. Gray: Vector Quantization and Signal Compression. Kluwer Academic (1992)
14. T. D. Lookabaugh, R. M. Gray: High-resolution Theory and the Vector Quantizer Advantage. IEEE Trans. On Information Theory, no. 35, (1989) 1020-1033
15. E. Forgy: Cluster analysis of multivariate data: Efficiency vs. interpretability of classifica-tions. Biometrics, vol. 21, no. 768, (1965)
16. A. P. Dempster, N. M. Laird, D. B. Rubin: Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society B, vol. 39, no. 1, (1977) 1-38
17. B. S. Manjunath, Aerial photo image database, http://vision.ece. ucsb.edu/datasets/
18. B. S. Manjunath, W. Y. Ma: Texture features for browsing and retrieval of image data. IEEE PAMI, vol. 18, no. 8, (1996) 837-842