Background subtraction - Joint domain range modeling code
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max_flow_min_cut_wrapper
siltp_color_hybrid
utils
MRF_mincut_clean.m
README.txt
calculate_kde_likelihood_sharpening.m
calculate_kde_likelihood_sharpening_Sheikh_normalization.m
calculate_kde_likelihood_sharpening_cvpr.m
cc.m
classify_using_kde_sharpening_sigma.m
classify_using_kde_sharpening_sigma_3_classes.m
classify_using_kde_sharpening_sigma_Sheikh_normalization.m
classify_using_kde_sharpening_sigma_cvpr.m
classify_using_kde_sharpening_sigma_q
classify_using_kde_sharpening_sigma_with_cache.m
classify_using_kde_sharpening_sigma_with_cache_cvpr.m
combine_multires_masks.m
get_gray_kernel_points_2d_from_image.m
get_kernel_points_2d_from_image.m
license.txt
load_video.m
run_domain_range_bg_modeling.m
run_domain_range_bg_modeling_hybrid.m
selective_calculate_kde_likelihood_bg_fg_with_cache.m
selective_calculate_kde_likelihood_bg_fg_with_cache_cvpr.m
selective_calculate_kde_likelihood_sharpening.m
selective_calculate_kde_likelihood_sharpening_cvpr.m
update_kde_model.m

README.txt

(1) Instructions to run code    
    (i) Main script is run_domain_range_bg_modeling.m (for color feature model only)
    or run_domain_range_bg_modeling_hybrid.m (for color-texture hybrid feature
    model)
    (ii)  Change video number to select appropriate video in above scripts. 
    Note that video_numbers can be an array of video numbers which will be 
    processed in sequence
    (ii)  Change path to input video, input groundtruth, output folder in load_video.m
    (iii) Set algorithm_to_use variable in the main script
    (iv)  Set any optional parameters in the main script
    Setting display_general to 1 displays the segmentation for each frame in a figure
    (v)   Run main script in matlab. 
    Note that it may take several hours for complete video
    sequence to be processed. If you desire to run script for a smaller subset of
    video frames, change the total_num_frames variable in load_video.m
    (vi) Output will be saved in the folder specified by
    output_sequences_folder in load_video.m
    Output is a mat file with all processed frames, all output frames, bg
    masks, and some input parameters. 
    (vii) Output video segmentation may be observed by using 
    step_through_color_image_sequence_pair( image_stack, segmented_image_stack);
    This is automatically invoked if a single video is in video_numbers. If
    multiple videos are processed, then the user has to load the mat files
    that were saved and then call step_through_color_image_sequence_pair
    manually

(2) Credits for third-party code used
    Thanks to Michael Rubinstein for max flow code (obtained from
    matlabcentral)
    Thanks to Mark A. Ruzon for RGB2Lab code (obtained from matlabcentral)