文件列表:

semi_pami10\interactive_image_seggentation_and_matting\001.bmp (205496, 2008-08-17)
semi_pami10\interactive_image_seggentation_and_matting\001_label.bmp (205494, 2008-08-26)
semi_pami10\interactive_image_seggentation_and_matting\002.bmp (48822, 2008-04-30)
semi_pami10\interactive_image_seggentation_and_matting\002_label.bmp (48822, 2008-08-26)
semi_pami10\interactive_image_seggentation_and_matting\calculate_how_many_nonzeros.m (954, 2010-10-07)
semi_pami10\interactive_image_seggentation_and_matting\calculate_index_for_sparse.m (1393, 2010-10-07)
semi_pami10\interactive_image_seggentation_and_matting\calcu_only_spatial_neighorhood.m (1721, 2010-10-07)
semi_pami10\interactive_image_seggentation_and_matting\construct_global_error_matrix_for_image.m (2348, 2010-10-06)
semi_pami10\interactive_image_seggentation_and_matting\construct_global_error_matrix_for_image_opt.m (2984, 2013-06-07)
semi_pami10\interactive_image_seggentation_and_matting\construct_local_error_matrix_for_image.m (5241, 2013-06-14)
semi_pami10\interactive_image_seggentation_and_matting\cvrt_img_to_matrix.m (678, 2010-10-06)
semi_pami10\interactive_image_seggentation_and_matting\demo_interactive_image_matting.m (3001, 2013-06-14)
semi_pami10\interactive_image_seggentation_and_matting\demo_interactive_image_segmentation.m (2707, 2013-06-14)
semi_pami10\interactive_image_seggentation_and_matting\extract_label_from_image.m (967, 2010-10-06)
semi_pami10\interactive_image_seggentation_and_matting\lsr_for_interactive_image.m (6477, 2013-06-07)
semi_pami10\interactive_image_seggentation_and_matting\my_binary_search.m (475, 2010-10-07)
semi_pami10\interactive_image_seggentation_and_matting\regularized_matrix.m (820, 2010-10-07)
semi_pami10\interactive_image_seggentation_and_matting\svd_local.m (600, 2013-06-07)
semi_pami10\semi_supervised_classification\arrange_data.m (547, 2010-10-06)
semi_pami10\semi_supervised_classification\a_iris.mat (6184, 2006-10-25)
semi_pami10\semi_supervised_classification\a_iris_random_splits.mat (207, 2010-10-06)
semi_pami10\semi_supervised_classification\a_vote.mat (30800, 2004-04-13)
semi_pami10\semi_supervised_classification\a_vote_random_splits.mat (268, 2010-10-06)
semi_pami10\semi_supervised_classification\calcu_distance_matrix_iter.m (784, 2010-10-06)
semi_pami10\semi_supervised_classification\construct_global_error_matrix.m (3531, 2013-06-07)
semi_pami10\semi_supervised_classification\construct_label_matrix.m (560, 2010-10-06)
semi_pami10\semi_supervised_classification\construct_local_error_matrix.m (4170, 2013-06-14)
semi_pami10\semi_supervised_classification\demo_iris_multi_class.m (2907, 2013-06-14)
semi_pami10\semi_supervised_classification\demo_vote_two_class.m (4009, 2013-06-14)
semi_pami10\semi_supervised_classification\lsr_for_multi_class.m (5670, 2013-06-07)
semi_pami10\semi_supervised_classification\lsr_for_two_class.m (5857, 2013-06-07)
semi_pami10\semi_supervised_classification\regularized_matrix.m (691, 2010-10-06)
semi_pami10\semi_supervised_classification\svd_local.m (600, 2010-10-06)
semi_pami10\interactive_image_seggentation_and_matting (0, 2013-06-13)
semi_pami10\semi_supervised_classification (0, 2013-06-13)
semi_pami10 (0, 2013-06-13)

====================================================================================================== Basic Information ====================================================================================================== This code is written by Shiming Xiang Address: National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, *** Email: smxiang@nlpr.ia.ac.cn.cn smxiang@gmail.com This software can be used freely for research purposes. Published reports of research using this code (or a modified version) should cite the article that describes the algorithm: Shiming Xiang, Feiping Nie, and Changshui Zhang. Semi-Supervised Classification via Local Spline Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, no.11, pp.2039-2053, 2010. This code has been compiled and tested using matlab 6.5 and matlab 7.0 version 1.0, rewritten and updated by Shiming Xiang, Oct. 7, 2010 ====================================================================================================== How to use ====================================================================================================== 1. for multi-class classification problems, please call function: lsr_for_multi_class(); 2. for two-class classification problems, please call function: lsr_for_two_class(); 3. There are two demo function. You can directly rum them. Also there are comments in the codes which may be helpful. demo_iris_multi_class.m demo_vote_two_class.m

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