Object Recognize by Column Vector Based on Rearrange Chain-Code

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This study deals with how to retrieve images using a rearranged chain-code based column vector. The chain-code expresses objects or boundaries of areas in straight chains with fixed direction and length, and encodes the final boundary, which is sensitive to rotated images. This study, therefore, works with chain-codes of eight directions in images and uses differences between values of chain-codes to obtain newly-rearranged chain-codes for image retrieval.

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768-773

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January 2010

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Flicker, et al. Query by image and video content: The QBIC system, IEEE Compuer magazine, 28(9): 23-32, (1995).

Google Scholar

[2] Yong Rui and Thomas S. Huang, Image retrieval: Current technologies, promising directions, and open issues, Journal of Visual Communication and Image Representation, vol. 10, pp.39-62, (1999).

DOI: 10.1006/jvci.1999.0413

Google Scholar

[3] Arnold WM Smeulders, marcel Worring, Simone Santini, Amarnath Gupta, and Ramesh Jain, Content-based image retrieval at the end of the early years, IEEE Transactions of Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp.1349-1380, (2000).

DOI: 10.1109/34.895972

Google Scholar

[4] Joo Chan-Hye, 8AB Representation of Spatial Relations between Objects for Content Based Image Retrieval, Korea, Advanced Institute of Science and Technology, (2005).

Google Scholar

[5] A.K. Jain and A. Vailaya, Image retrieval using color and shape, , Pattern Recognition, vol. 29, No. 8, pp.1233-1244, (1996).

DOI: 10.1016/0031-3203(95)00160-3

Google Scholar

[6] Theo Gevers and Arnold W.M. Smeulders, PicToSeek: Combining color and shape invariant features for image retrieval, IEEE Transactions on Image Processing, vol. 9, No. 1, pp.102-119, January (2001).

DOI: 10.1109/83.817602

Google Scholar

[7] M. Swain and D. Ballard, Color indexing, International Journal of Computer Vision, 7(1), pp.11-32, (1991).

Google Scholar

[8] J. Hafner, H. Sawhney, W. Equitz, M. Flickner and W. Niblack, Efficient color histogram indexing for quadratic form distance functions, IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 17, no. 7, pp.729-736, July (1995).

DOI: 10.1109/34.391417

Google Scholar

[9] M. Carlotto, Histogram analysis using a Scale-space approach, IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 9, no. 1, pp.121-129, (1987).

DOI: 10.1109/tpami.1987.4767877

Google Scholar

[10] Jing Huang, S Ravi Kumar, Mandar Mitra, Wei-Jing Zhu and Ramin Zabi, Image indexing using color correlograms, Computer Vision and Pattern Recognition, 1997. Proceedings, 1997 IEEE Computer Society Conference onJune (1997).

DOI: 10.1109/cvpr.1997.609412

Google Scholar