Abstract
We describe a hierarchical linear subspace method to query large on-line image databases using image similarity as the basis of the queries. The method is based on the generic multimedia indexing (GEMINI) approach which is used in the IBM query through the image content search system. Our approach is demonstrated on image indexing, in which the subspaces correspond to different resolutions of the images. During content-based image retrieval, the search starts in the subspace with the lowest resolution of the images. In this subspace, the set of all possible similar images is determined. In the next subspace, additional metric information corresponding to a higher resolution is used to reduce this set. This procedure is repeated until the similar images can be determined. For evaluation we used three image databases and two different subspace sequences.
Similar content being viewed by others
References
Bei, C.-D., & Gray, R. M. (1985). An improvement of the minimum distortion encoding algorithm for vector quantization. IEEE Transactions on Communications, 33(10), 1132–1133.
Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (pp. 127–134).
Böhm, C., Berchtold, S., & Kei, D., A. K. (2001). Searching in high-dimensional spaces—Index structures for improving the performance of multimedia databases. ACM Computing Surveys, 33(3), 322–373.
Burt, P. J., & Adelson, E. H. (1983). The laplacian pyramidas a compact image code. IEEE Transactions on Communications, 31(4), 532–540.
Chen, Y., & Wang, J. Z. (2004). Image categorization by learning and reasoning with regions. Journal of Machine Learning Research, 5, 913–939.
Dunckley, L. (2003). Multimedia databases, an object-rational approach. Reading, MA: Addison-Wesley.
Faloutsos, C. (1999). Modern information retrieval. In R. Baeza-Yates & B. Ribeiro-Neto (Eds.), Modern information retrieval, Chap. 12 (pp. 345–365). Reading, MA: Addison-Wesley.
Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petkovic, D., et al. (1994). Efficient and effective querying by image content. Journal of Intelligent Information Systems, 3(3/4), 231–262.
Flickner, M., Sawhney, H., Niblck, W., Ashley, J., Huang, Q., Dom, B., et al. (1995). Query by image and video content the QBIC system. IEEE Computer (pp. 23–32).
Gonzales, R. C., & Woods, E. W. (2001). Digital image processing (2nd ed.). New Jersey: Prentice Hall.
Guan, L., & Kamel, M. (1992). Equal-average hyperplane partitioning method for vector quantization of image data. Pattern Recognition Letters, 13, 693–699.
Hove, L.-J. (2004). Extending image retrieval systems with a thesaurus for shapes. Master’s thesis, Institute for Information and Media Sciences, University of Bergen.
Jedrzejek C., C. L. (1995). Fast closest codewordsearch algorithm for vector quantization. In Proc. of the IEEE Information Theory Workshop ITW’95.
Jeon, J., Lavrenko, V., & Manmatha, R. (2003). Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 119–126).
Kim, H.-G., Moreau, N., & Sikora, T. (2005). MPEG-7 audio and beyond: Audio content indexing and retrieval. New York: Wiley.
Kimura, A., Kawanish, T., & Kashino, K. (2004). Acceleration of similarity-based partial image retrieval using multistage vector quantization. In Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04), vol. 2 (pp. 993–996).
Lang, S. (1970). Linear algebra. Reading, MA: Addison-Wesley.
Lee, C. H., & Chen, L. H. (1994). Fast closeset codewordsearch algorithm for vector quantization. In IEE Proceedings Vision Image and Signal Processing, vol. 141 (pp. 143–148).
Lee, C.-H., & Chen, L.-H. (1995). A fast search algorithm for vector quantization using mean pyramids of codewords. IEEE Transactions on Communications, 43(3–5), 1697–1702.
Li, J., & Wang, J. (2003a). Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Learning, 25(9), 1075–1088.
Li, J., & Wang, J. (2003b). Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Transactions on Image Processing, 13(3), 340–353.
Manjunath, B., Salembier, P., & Sikora, T. (2002). Introduction to MPEG-7: Multimedia content description interface. New York: Wiley.
Mirmehdi, M., & Periasamy, R. (2001). CBIR with perceptual region features. In BMVC.
Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E. H., Petkovic, D., et al. (1993). The QBIC project: Querying images by content, using color,texture, and shape. In Storage and Retrieval for Image and Video Databases (SPIE) (pp. 173–187).
Orchard, M. D. (1991). A fast near-neighbour search algorithm. In In Proc. of IEEE ICASSP (pp. 2297–2300).
Quack, T., Mönich, U., Thiele, L., & Manjunath, B. S. (2004). Cortina: A system for large-scale, content-based web image retrieval. In Proceedings of the 12th Annual ACM International Conference on Multimedia (pp. 508–511).
Russell, S. J., & Norvig, P. (2003). Artificial intelligemce: A modern approach (2nd ed.). New Jersey: Prentice-Hall.
Skarbek, W., & Ignasiak, K. (1996). Fast vq codebook search in klt space. Neural Network World, 6(3), 383–386.
Smeulders, A., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349–1380.
Tuncel, E., Ferhatosmanoglu, H., & Rose, K. (2002). Vq-index: an index structure for similarity searching in multimedia databases. In Proceedings of the tenth ACM international conference on Multimedia MULTIMEDIA ’02 (pp. 543–552).
Wang, J., Li, J., & Wiederhold, G. (2001). Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947–963.
Wang, J. Z., Wiederhold, G., Firschein, O., & Wei, S. X. (1997). Content-based image indexing and searching using daubechies wavelets. International Journal on Digital Libraries, 1(4), 311–328.
Winston, P. H. (1992). Artificial intelligence (3rd ed.). Reading, MA: Addison-Wesley.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wichert, A. Content-based image retrieval by hierarchical linear subspace method. J Intell Inf Syst 31, 85–107 (2008). https://doi.org/10.1007/s10844-007-0041-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10844-007-0041-4