由於數位影音資料庫的日益增加，如何有效管理就顯得更加重要。CBIR(content based image retrieval) 是以影像內容為基礎的影像擷取系統。而影像切割在CBIR也得系統也是很重要的，所有的Query image 都會執行此一步驟。因此我的主要目標是要將一張圖片的背景和前景分開，使得CBIR比對時可以提高效率，並且得到更好的結果。

As digital audio and video databases increasing, how to effectively manage all the more important. CBIR (content based image retrieval) of video content-based image retrieval system. The images have to cut in CBIR system is also very important, all the Query image will implement this step. So my main goal is to make a picture from the background and prospects, than to make CBIR can improve efficiency and better results.

[1] Jing Zhang; Seok-Wun Ha; Leopard Image Retrieval using Region of Interest and Texture Feature, Hybrid Information Technology, 2006. ICHIT '06. International Conference on, Nov. 2006 Digital Object Identifier 10.1109 / ICHIT. 2006. 253504

[2] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Texture features for image classification,” IEEE Trans. on Syst. Man Cybern., vol. 3, pp. 610-621, Nov. 1973.

[3] D.A. Clausi and M.E. Jernigan, “ A fast method to determine co-occurrence
texture features,” IEEE Trans. Geosci. Remote Sensing, vol. 36, pp. 298-300, Feb. 1998.

[4] Pappas, T.N.; Junqing Chen; Depalov, D.; Perceptually based techniques for image segmentation and semantic classification Communications Magazine, IEEE Volume 45, Digital Object Identifier 10.1109/MCOM.2007.284537

[5] C. Carson et al., “Blobworld: Image Segmentation Using Expectation-Maximizations and It Application to Image Querying,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, Aug. 2002, pp. 1026–38.

[6] T. N. Pappas, “An Adaptive Clustering Algorithm for Image Segmentation,” IEEE Trans. Sig. Proc., vol. SP-40, Apr. 1992, pp. 901–14.

[7] A.P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. Royal Statistical Soc. B, vol. 39, pp.

[8] R. Fergus, P. Perona, and A. Zisserman, “Object Class Recognition by Unsupervised Scale-Invariant Learning,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2003.

[9] L. Hermes, T. Zo ller, and J.M. Buhmann, “Parametric Distributional Clustering for Color Image Segmentation,” Proc. European Conf. Computer Vision (ECCV ’02), A. Heyden, G. Sparr, M. Nielsen, and P. Johansen, eds., pp. 577-591, 2002.

[10] J. Malik, S. Belongie, T. Leung, and J. Shi, “Contour and Texture Analysis for Image Segmentation,” Int’l J. Computer Vision, vol. 43, no. 1, pp. 7-27, 2001.

[11] Muwei Jian, Junyu Dong, Ruichun Tang, Combing Color, Texture and Region With Objects of User’s Interest for Content-Based Image Retrieval,2007 IEEE, Department of Computer Science, Ocean University of China.

[12] Carson, C., Belongie, S., Greenspan, H., 2002. Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. PAMI 24 (8), 1026–1038.

[13] A. K. Jain, and F. Farroknia, "Unsupervised texture segmentation using Gabor filters," Pattern Recognition, Vo.24, No.12,pp.1167-1186,1991

[14] G.Hamerly, C. Elkan, "Alternatives to the k-means algorithm that find better clusterings", Proc. of the ACM Conference on Information and Knowledge Management, CIKM-2002, 2002, 600-607.

[15] R.H. Turi, "Clustering-based color image segmentation", PhD Thesis, Monash University, Australia 2001.

[16] Morales, D.I.; Moctezuma, M.; Parmiggiani, F.; Urban edge detection by texture analysisGeoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International Volume 6, 2004 Page(s):3826 - 3828 vol.6 Digital Object Identifier 10.1109/IGARSS.2004.1369957

[17] D.A. Clausi, and Yongping Zhao: “Rapid Determination of Co-occurrence Texture Features”, Proc. of the IEEE IGARSS’01, pp. 1880-1882, 2001.

[18] Clausi, D.A.; Yongping Zhao; An advanced computational method to determine co-occurrence probability texture featuresGeoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International Volume 4, 24-28 June 2002 Page(s):2453 - 2455 vol.4

[19] Pappas, T.N.: ‘An adaptive clustering algorithm for image segmentation’, IEEE Trans. Signal Process.

[20] J. Xie, and H. T. Tsui, Image segmentation based on maximum-likelihood estimation and optimum entropy distribution (MLE–OED), Pattern Recognition Letters, Volume 25, Issue 10 , 16 July 2004, pp. 1133-1141.

[21] Y.N. and Manjunath, B.S., 2001. Unsupervised segmentation of color–texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 238, pp. 800–810.