LIBSVX and Video Segmentation Evaluation
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1 CVPR 14 Tutorial! 1! LIBSVX and Video Segmentation Evaluation Chenliang Xu and Jason J. Corso!! Computer Science and Engineering! SUNY at Buffalo!! Electrical Engineering and Computer Science! University of Michigan!
2 Talk Highlights 2! LIBSVX Library Methods! Five offline supervoxel methods! One streaming supervoxel method (v2.0)! Evaluation Benchmark! A set of 2D frame-by-frame & 3D volumetric metrics! Human independent metrics! Flattening Algorithm: Uniform Entropy Slice (v3.0)! Updates & Recognition (v3.1)! [Xu & Corso CVPR 2012]! [Xu, Xiong & Corso ECCV 2012]! [Xu, Whitt & Corso ICCV 2013]!
3 LIBSVX: Supervoxel Methods 3!
4 LIBSVX: Supervoxel Methods 4! Offline Algorithms:! Graph-Based (GB)!![Felzenszwalb & Huttenlocher IJCV 2004]! Graph-Based Hierarchical (GBH)!![Grundmann et al. CVPR 2010]! Segmentation by Weighted Aggregation (SWA)!![Sharon et al. CVPR 2001, NATURE 2006], [Corso et al. TMI 2008]! Nyström Normalized Cuts (NCut)!![Fowlkes et al. TPAMI 2004] [Shi & Malik TPAMI 2000]! Mean Shift (External at the author s website)!![paris & Durand CVPR 2007]! Streaming Algorithm:! Graph-Based Streaming Hierarchical (streamgbh)!![xu, Xiong & Corso ECCV 2012]!
5 Data Format 5! Input Video! Extract frames in png and ppm formats.! Organize in a folder as a sequence of images: %05d.png/ppm.! Example: ffmpeg -i video.mp4 input/%05d.ppm! Output Segmentation! A sequence of color-coded images. Each supervoxel index or label is coded with a unique RGB color.! Use read_video_supervoxels.m to convert a folder of segmentation frames to a 3D matrix in Matlab, and use cvlbmap.m to sort the labels to 1:N.!
6 Nyström Normalized Cuts (NCut) 6! Implement: MATLAB! To Run: Nystrom_video(PathInput, PathOutput, numofsvx, numofsamples, numofevecs, sigmae, sigmalab, KNN)! PathInput & PathOutput Paths to input and output directories.! numofsvx number of supervoxels! numofsamples number of sampled points in a video! numofevecs: number of eigenvectors! sigmae weight of Euclidean distance! sigmalab weight of Lab color distance! KNN the option to use knn to assign final labels! [Fowlkes et al. TPAMI 2004] [Shi & Malik TPAMI 2000]!
7 Nyström Normalized Cuts (NCut) 7! Demo videos.!
8 Segmentation by Weighted Aggregation (SWA) 8! Implement: C/C++! To Run:./swa config.txt! The following parameters are to be set in config.txt file.! InputSequence Specifies the input sequence naming format, e.g. input/%05d.png.! Frames Frame number to be processed.! MaxStCubeSize The number of frames the spatiotemporal cube that spans back from frame N.! NumOfLayers Number of layers in the SWA hierarchy.! VizLayer It specifies the layers of the hierarchy to be visualized.! VizFileName The directory path for storing the visualization results.! [Sharon et al. CVPR 2001, NATURE 2006], [Corso et al. TMI 2008]!
9 9! Segmentation by Weighted Aggregation (SWA) Demo videos.!
10 Graph-Based (GB) and Hierarchical (GBH) 10! Implement: C/C++! To Run:./gbh c c_reg min sigma hie_num input output! c Governs the merging threshold of the two nodes in the minimum spanning tree during the oversegmentation stage. Bigger C means larger segments.! c_reg Like c, it governs the merging of two nodes, but this is in the hierarchical levels whereas c is at the pixel level nodes.! min Enforced minimum segment size for a whole supervoxel.! sigma The variance of the Gaussian smoothing.! hie_num The number of desired levels in the hierarchy + 1. If hie_num = 0, no hierarchy is created, which is GB.! [Grundmann et al. CVPR 2010]! [Felzenszwalb & Huttenlocher IJCV 2004]!
11 11! Graph-Based (GB) Demo videos.!
12 12! Graph-Based Hierarchical (GBH) Demo videos.!
13 Graph-Based Streaming Hierarchical (streamgbh) 13! Implement: C/C++! To Run:./gbh_stream c c_reg min sigma range hie_num input output! range The number of frames to include in one subsequence (or clip). range = 1 means each frame is handled separately. range = number_of_frames means the whole video is handled at once. range = k is the typical way to run the streaming method.! Other parameters are just like in GBH.! The program also streams the output of supervoxel segmentation for subsequences.! [Xu, Xiong & Corso ECCV 2012]!
14 14! Graph-Based Streaming Hierarchical (streamgbh) Demo videos.!
15 LIBSVX: Benchmark Evaluation 15!
16 3D Undersegmentation Error 16! It measures what fraction of the pixels exceed the boundary of the ground-truth segmentation.! Ground-truth segmentation:! G = {g 1,g 2,...,g m } Supervoxels:! S = {s 1,s 2,...,s n } 3D Undersegmentation error:! UE(g i )= P n j=1 Vol(s j s j \ g i 6= ;) Vol(g i ) Vol(g i ) where Vol( ) denotes the number of the voxels that are inside a 3D volume.! We take the average across all ground-truth segments, where they are equally weighted.!
17 3D Segmentation Accuracy 17! It measures what fraction of a ground-truth segment is correctly segmented by the supervoxels: each supervoxel belongs to only one object or ground-truth segment.! 3D Segmentation Accuracy:! SA(g i )= P n j=1 Vol(s j)1{vol(s j \ g i ) Vol(s j \ ḡ i )} Vol(g i ) where ḡ i = G g i means all other segments.! To evaluate the segmentation accuracy of a video, we again take the average of the segmentation accuracy score across all ground-truth segments.!
18 3D Boundary Recall i i +1 Within-frame boundary! Object boundary defined in! 2D images.! Boundary Disappears Between-frame boundary! A same object in adjacent! two frames.!! Boundary Appears The volumetric 3D boundary recall:! S \ G R = G S and G are the 3D boundary maps for segments and GT.! \ solves a bipartite graph assignment between two boundary maps (often relaxed by 1 pixel offset).! Video at frame i 3D Boundary 18!
19 Human-Independent Metrics 19! Explained Variation considers the supervoxels as a compression method of a video.! Mean value of the voxels assigned to! the supervoxel that contains.! x i The global voxel mean.! R 2 = The actual video voxel value.! Pi (µ i µ) 2 P i (x i µ) 2 [Moore et al. CVPR 2008]! Supervoxel Mean Duration measures the temporal extension of supervoxels. It measures the average length of supervoxels in a video.!
20 Computational Cost 20! Computational Cost in terms of time and peak memory consumption.! GB GBH streamgbh SWA MeanShift NCut Time (s) Memory (GB) We report the computational cost of all methods for a typical video with 352 x 288 x 85 voxels. The experiment is done on a laptop featured with Intel Core 2.70GHz and 32GB RAM running Linux.! All methods are running in single thread except NCut running with 8 threads with resized spatial resolution to 240 x 160.!
21 Evaluate your method with benchmark 21! Dataset Setup! We include the Chen xiph.org dataset in the benchmark.! SegTrack and GaTech datasets can be downloaded and organized in a same file hierarchy.! Preparing your results for use with the benchmark.! Compute supervoxels for a complete dataset.! Run with a varying set of parameters.! Segment each video with a distribution of supervoxel numbers varying from less than 200 to more than 900.! We use a linear interpolation scheme to compute the values for the curve. More samples leads to better estimate of the curve.!
22 Evaluate your method with benchmark 22! Let the root path of your results for Chen's data set be ROOT.! The eight videos in the root path.! ROOT/bus! ROOT/container! ROOT/garden!! ROOT/ice! ROOT/paris!!! ROOT/salesman! ROOT/soccer!! ROOT/stefan! The results for one video are put in different folders with the supervoxel number as the name in this example.! ROOT/bus/150! ROOT/bus/227!! ROOT/bus/905! The segmentation results of one video with one particular supervoxel number! ROOT/bus/150/00001.ppm!! ROOT/bus/150/00085.ppm!
23 Evaluate your method with benchmark 23! Config and run EVALUATION.m!!path_input_method = ROOT; % path to your results of one data set!!path_ppm = 'dataset/chen_ppm'; % path to dataset!!dataset = 1; % 1 Chen's xiph.org; 0 SegTrack; 2 GaTech!!output_path = path_to_save_your_evaluation_results';!!verbose = 1; % option to show intermediate results!!x_min = 200; x_max = 900; % range of supervoxels generated by your method!! It generates both 2D frame-by-frame and 3D volumetric scores.! To compare with scores by methods in the library, see XuCorso_CVPR2012_mat!!!!
24 Other Video Segmentation Evaluation 24! A Unified Video Segmentation Benchmark! Dataset: BVSD (100 videos)! Labeled ground-truth frames at every 20 th frames.! Each is labeled by multiple human annotators.! Spatiotemporal coherence is preserved.! Evaluation Metrics! Boundary Precision-Recall (BPR)! 2D image segment boundaries.! Volume Precision-Recall (VPR)! [Sundberg et al. CVPR 2011]! [Galasso et al. ICCV 2013]! Treated as volumes at the ground-truth frames in a video.! Related to 3D undersegmentation error and 3D segmentation accuracy.!
25 LIBSVX: Flattening Hierarchy 25!
26 Prerequisites 26! Hierarchical Video Segmentation! LIBSVX: GBH or SWA (treeify)! GaTech web service: Solver for Binary QP! IBM ILOG CPLEX Optimization Studio V Feature Criteria! Ce Liu s Optical Flow Aug 1, Objectness V PFF DPM V
27 Usage 27! To Run: ues(video_path, hie_path, output_path, hie_select_num, sigma, method, visflag)! video_path: path to raw video extracted png frames.! hie_path: path to hierarchical segmentation output.! output_path: folder to output flattening results.! hie_select_num: select a certain number of levels from the hierarchical segmentation as input to the flattening algorithm.! sigma: the weight between unary and binary.! method: feature criterion of motion-ness/object-ness/human-ness! visflag: option to output intermediate results.! Treeify a Hierarchy! It modifies arbitrary supervoxel hierarchy to a tree structure, such as SWA. See treeify.m.! Enforce the supervoxel boundary agreement across levels in a hierarchy.!
28 28! Demo: boxers Motion-ness! flow feature! selection! flattening!
29 29! Demo: danceduo Motion-ness! flow feature! selection! flattening!
30 Demo: danceduo 30! Object-ness! object-ness! selection! flattening!
31 31! Demo: danceduo Human-ness! human detection! selection! flattening!
32 32! LIBSVX v3.1: Updates & Recognition Task
33 LIBSVX v3.1: Updates & Recognition Task 33! New Datasets:! SegTrack v2 Updated version of the SegTrack and provides frame-by-frame pixel-level multiple foreground objects labeling. It contains 14 video sequences.! [Li et al. ICCV 2013]! BVDS 100 videos with multiple human annotations by a sampling rate of every 20 frames.! [Galasso et al. ICCV 2013]! CamVid 18K frames with labeled 11 semantic object class labels at 1 Hz and in part 15 Hz.! [Brostow et al. ECCV 2008]! New Metrics:! Supervoxel Size Variation.! [Chang et al. CVPR 2013]!
34 34! LIBSVX v3.1: Updates & Recognition Task Recognition Task on CamVid dataset.! GB! GBH! streamgbh! SWA! TSP! MeanShift!
35 LIBSVX v3.1: Updates & Recognition Task 35! Speed up the evaluation benchmark.! Of course: Bug fix to methods!! Planned release date: Fall 2014.!
36 Thank you! 36! Download LIBSVX:
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