JACIII Vol.10 No.2 pp. 136-144
doi: 10.20965/jaciii.2006.p0136


Designing Image Retrieval System with the Concept of Visual Keys

Manabu Serata, Yutaka Hatakeyama, and Kaoru Hirota

Hirota Laboratory, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan

January 14, 2005
September 25, 2005
March 20, 2006
content based image retrieval, text retrieval technique, clustering

A concept of visual keys is proposed to provide efficient and useful content-based image retrieval systems to users. Visual keys are defined as representative sub-images which are extracted from an image database by using image feature clustering. The proposed system is implemented and is tested on 1,000 images, which are included in the COREL database. Although the system makes use of only 80 sub-images from 8,962 ones extracted from the image database, the performance is kept with 90%. The retrieval time is within 4ms on the proposed system, which has retrieval efficiency like that of text retrieval by being applied text retrieval techniques, and thus the system is expected to provide the services on the WWW.

Cite this article as:
Manabu Serata, Yutaka Hatakeyama, and Kaoru Hirota, “Designing Image Retrieval System with the Concept of Visual Keys,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.2, pp. 136-144, 2006.
Data files:
  1. [1] S. Brandt, J. Laaksonen, and E. Oja, “Statistical Shape Features in Content-Based Image Retrieval,” In Proc. ICPR2000, Sep., 2000.
  2. [2] C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein, and J. Malik, “Blobworld: A System for Region-Based Image Indexing and Retrieval,” In Proc. Visual Information Systems, pp. 509-516, June, 1999.
  3. [3] M. Flickner, H. Sawhney, W. Niblack, C. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by Image and Video Content: The QBIC System,” IEEE computer, 28(9), 1995.
  4. [4] W. Frakes, and R. Basza-Yates, “Information Retrieval: Data Structures and Algorithms,” PrenticeHall, New Jersey, 1992.
  5. [5] J. Laaksonen, E. Oja, M. Koskela, and S. Brandt, “Analyzing Low-Level Visual Features Using Content-Based Image Retrieval,” In Proc. ICONIP 2000, pp. 1333-1338, Taejon, Korea, Nov., 2000.
  6. [6] T. P. Minka, and R. W. Picard, “Interactive Learning Using a Society of Models,” Pattern Recognition, 30(3), pp. 565-581, 1997.
  7. [7] M. Stricker, and M. Orengo, “Similarity of color images Storage and Retrieval for Image and Video Databases III,” In Proc. SPIE, Vol.2420, pp. 381-392, 1995.
  8. [8] A. Pentland, R. W. Picard, and S. Sclaroff, “Photobook: Tools for Content-Based Manipulation of Image Databases,” In Proc. SPIE, Vol.2185, pp. 34-47, Feb., 1994.
  9. [9] R. W. Picard, and T. Kabir, “Finding Similar Patterns in Large Image Databases,” In Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, Vol.5, pp. 161-164, 1993.
  10. [10] J. J. Rocchio, “Relevance Feedback in Information Retrieval,” In G. Salton, editor, The SMART Retrieval System – Experiments in Automatic Document Processing, chapter 14, pp. 313-323, Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1971.
  11. [11] G. Salton, and C. Buckley, “Term Weighting Approaches in Automatic Text Retrieval,” Information Processing and Management, 24(5), pp. 513-523, 1998.

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