Abstract
Object-level image retrieval is an active area of research. Given an image, a human observer does not see random dots of colors. Rather, he/she observes familiar objects in the image. Therefore, to make image retrieval more user-friendly and more effective and efficient, object-level image retrieval technique is necessary. Unfortunately, images today are mostly represented as 2D arrays of pixels values. The object-level semantics of the images are not captured. Researchers try to overcome this problem by attempting to deduce the object-level semantics through additional information such as the motion vectors in the case of video clips. Some success stories have been reported. However, deducing object-level semantics from still images is still a difficult problem. In this paper, we propose a “color-spatial” approach to approximate object-level image retrieval. The color and spatial information of the principle components of an object are estimated. The technique involves three steps: the selection of the principle component colors, the analysis of spatial information of the selected colors, and the retrieval process based on the color-spatial information. Two color histograms are used to aid in the process of color selection. After deriving the set of representative colors, spatial knowledge of the selected colors is obtained using a maximum entropy discretization with event covering method. A retrieval process is formulated to make use of the spatial knowledge for retrieving relevant images. A prototype image retrieval tool has been implemented on the Unix system. It is tested on two image database consisting of 260 images and 11,111 images respectively. The results show that the “color-spatial” approach is able to retrieve similar objects with much better precision than the sole color-based retrieval methods.
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References
T. Asano and N. Yokoya, “Image segmentation schema for low-level computer vision,” Pattern Recognition, Vol. 14, pp. 267–273, 1981.
I. Biederman, “Human image understanding: Recent research and a theory. Computer Vision, Graphics, and Image Processing,” Vol. 32, pp. 29–73, 1985.
E. Binaghi, I. Gaglardi, and R. Schettini, “Indexing and fuzzy logic-based retrieval of colour images,” in: Visual Database Systems, Vol. 2: IFIP, Elsevier Science Publishers R.V. (Holland), 1992, pp. 79–92.
D.K.Y. Chiu and T. Kolodziejczak, “Synthesizing knowledge: A cluster analysis approach using eventcovering,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 16, No. 2, pp. 462–467, 1986.
T.S. Chua, S.K. Chan, H.K. Pung, and G.J. Lu, “A content-based image retrieval system,” Technical Report, Internal Report, DISCS, NUS, 1994.
T.S. Chua, S.K. Lim, and H.K. Pung, “Content-based retrieval of segmented images,” ACM Multimedia, pp. 211-218, Oct. 1994.
R.M. Haralick, “Digital step edges from zero crossing of second directional derivatie,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 6, pp. 58–68, 1984.
R.M. Haralick and G.L.Kelly, “Pattern recognition with measurement space and spatial clustering for multiple image,” Proc. IEEE, Vol. 57, pp. 654–665, 1969.
R.M. Haralick and L.G. Shapiro, “Survey: Image segmentation techniques,” Computer Vision, Graphics, and Image Processing, Vol. 29, pp. 100–132, 1984.
T. Kato, T. Kurita, and H. Shimogaki, “Intelligent visual interaction with image database systems-toward the multimedia personal interface,” Journal of Information Processing, Vol. 14, No. 2, pp. 134–143, 1991.
A. Klinger, “Data structures and pattern recognition,” in Proc. of the First International Joint Conference on Pattern Recognition, Oct. 1973, pp. 497-498.
B.M. Mehtre, M.S. Kankanhalli, A.D. Narasimhalu, and G.C. Man, “Color matching for image retrieval,” Pattern Recognition Letters, Vol. 16, pp. 325–331, 1995.
D.L. Milgram, “Region extraction using convergent evidence,” Computer Graphics Image Processing, Vol. 11, pp. 1–12, 1979.
A. Nagasaka and Y. Tanaka, “Automatic video indexing and full-video search for object appearances,” in: Visual Database Systems, Vol. 2: IFIP, Elsevier Science Publishers R.V. (Holland), 1992, pp. 113–127.
W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, G. Taubin, “The QBIC project: Querying images by content using colour, texture, and shape,” SPIE, Vol. 1908, pp. 173–187, 1993.
R. Ohlander, “Analysis of natural scenes,” PhD Thesis, Carnegie-Mellon University, Pittsburgh, 1975.
T. Pavlidis, “Segmentation of pictures and maps through functional approximation,” Computer Graphics Image Processing, Vol. 1, pp. 360–372, 1972.
W.K. Pratt, Digital Image Processing, 2nd edn., John Wiley, 1991.
G. Salton and M.J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill: New York, 1983.
R. Sekuler and R. Blake, Perception, 3rd edn., McGraw-Hill Inc., 1994.
P.L. Stanchev, A.W.M. Smeulders, and F.C.A. Groen, “An approach to image indexing of documents,” in: Visual Database Systems, Vol. 2: IFIP, Elsevier Science Publishers R.V. (Holland), 1992, pp. 63–77.
M.J. Swain and D.H. Ballard, “Color indexing,” International Journal of Computer Vision, Vol. 7, No. 1, pp. 11–32, 1991.
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Hsu, W., Chua, T. & Pung, H. Approximating Content-Based Object-Level Image Retrieval. Multimedia Tools and Applications 12, 59–79 (2000). https://doi.org/10.1023/A:1009692213403
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DOI: https://doi.org/10.1023/A:1009692213403