A Method for Automatic Object Extraction in High-resolution Remote Sensing Image

[1] Boykov Y and Jolly M P. Interactive graph cuts for optimalboundary region segmentation of objects in N-D images.International Conference on Computer Vision (ICCV),Vancouver, B C, Canada, 2001, 1: 105-112. [2] Rother C, Kolmogorov V and Blake A. GrabCut-Interactiveforeground extraction using iterated graph cuts. ACMSIGGRAPH, Los Angeles, USA, 2004: 309-314. [3] Paul Viola and Michael Jones. Rapid object detection using aboosted cascade of simple features. Proceeding of IEEEConference on CVPR, Kauai, Hawaii, USA, 2001, 1: 511-518. [4] Jerome Friedman, Trevor Hastie, and Robert Tibshirani.Logistic regression: A statistical view of boosting. The Annalsof Statistics, 2000, 28(2): 337-374. [5] Jamie Shotton, John M. Winn, Carsten Rother, and AntonioCriminisi. TextonBoost: joint appearance, shape and contextmodeling for multi-class object recognition and segmentation.ECCV, Graz, Austria, 2006: 1-15. [6] Jeff Bilmes. A gentle tutorial of the EM algorithm and itsapplication to parameter estimation for gaussian mixture andhidden markov models. ICSI TR-97-021, U.C. Berkeley, 1998. [7] Xu L. Bayesian-kullback coupled Ying-Yang Machines:unified learnings and new results on vector quantization.International Conference on Neural Information Processing(ICONIP95), Beijing, China, 1995: 977-988. [8] Xu L. Bayesian Ying-Yang machine, clustering and numberof clusters[J].Pattern Recognition Letters.1997, 18(11-13):1167-1178 [9] Goldberger J, Gordon S, and Greenspan H. An efficientimage similarity measure based on approximations ofkl-divergence between two gaussian mixtures. InternationalConference on Computer Vision (ICCV), Nice, France, 2003:487-494. [10] Orchard M and Bounman C. Color quantization of images[J].IEEE Trans. on Signal Processing.1991, 39(12):2677-2690