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Video Matching Using Spatiotemporal Volumes
Arslan Basharat, Yun Zhai, and Mubarak Shah, Content Based Video Matching Using Spatiotemporal Volumes, Journal of Computer Vision and Image Understanding (CVIU), Volume 110, Issue 3, June 2008, Pages 360-377, Similarity Matching in Computer Vision and Multimedia
Presentation Slides [PDF]
Dataset Used NEW!
This paper presents a novel framework for matching video sequences using the spatiotemporal segmentation of videos. Instead of using appearance features for region correspondence across frames, we use interest point trajectories to generate video volumes. Point trajectories, which are generated using the SIFT operator, are clustered to form motion segments by analyzing their motion and spatial properties. The temporal correspondence between the estimated motion segments is then established based on most common SIFT correspondences. A two pass correspondence algorithm is used to handle splitting and merging regions. Spatiotemporal volumes are extracted using the consistently tracked motion segments. Next, a set of features including color, texture, motion, and SIFT descriptors are extracted to represent a volume. We employ an Earth Moverís Distance (EMD) based approach for the comparison of volume features. Given two videos, a bipartite graph is constructed by modeling the volumes as vertices and their similarities as edge weights. Maximum matching of this graph produces volume correspondences between the videos, and these volume matching scores are used to compute the final video matching score. Experiments for video retrieval were performed on a variety of videos obtained from different sources including BBC Motion Gallery and promising results were achieved. We present qualitative and quantitative analysis of retrieval along with a comparison with two baseline methods.
Spatiotemporal Volume Extraction
Give a video, we are able to extract meaningful regions that correspond to moving foreground and background "objects". Following are the main steps performed to extract volumes from a video.
Given a video perform following steps to extract SIFT interest point trajectories:
Computation of SIFT interest point correspondence between neighboring frames,
Initial trajectory generation by merging point correspondences,
Removal of irregular trajectory segments,
Merging broken trajectories,
Removal of small trajectories.
Different motion segments (color coded) are determined using homography based motion segmentation. The proximity of interest points within a segmented region are used to group of points belonging to different objects. This video shows results of initial region detection along with trajectories generated in the previous step. The changing colors show that there is no correspondence information at this stage.
Tracking performed using the maximum trajectory membership constraint between detected regions. Figure illustrates the effect of the two pass tracking algorithm used on three scenarios of objects splitting and merging. (a) Types of relative motion of two segments in a video. (b) Forward labeling generates first set of labels by progressing the labels from the first to the last frame of the video. (c) Backward labeling is applied in the reverse direction to produce another set of labels. (d) Two sets of labels are merged to produce the final labels.
Spatiotemporal volumes are finally extracted by simply stacking together the tracked bounding boxes. Each row contains three sample frames from the video along with the extracted volume. Each bounding box represents one of the several regions being tracked in each video. The consistent color of the bounding box represents the accuracy of region tracking. We are also able to handle split and merge in case of two regions belonging to the airplane (second frame). Both of these regions (with same yellow label) contribute to the airplane volume shown on the extreme right.
Features extracted from each spatiotemporal volume include:
Motion of a volume is captured by eight quantized directions of motion of the member trajectories. (a) Rectangle captures a boat moving towards bottom left in the video. (b) The peaks correspond to left and bottom directions (pi to 3pi/2 out of [0, 2pi] range). Interest point trajectories are shown in red with blue end points.
Isodata clustering is performed in the feature space to generate respective feature descriptor. For instance, in case of the SIFT interest point descriptor, 128-dimensional space is used to generate a set of clusters. This set of clusters is generate signature representation which includes respective mean features and cluster population for each cluster.
Feature Comparison and Video Matching
Earth Mover's Distance is used to match two signatures of the same feature coming from two volumes from two different videos. For a particular feature, the similarity measure between two volumes is given by
which is used as the edge weight between the two volumes in our graphical representation of the videos as illustrated here:
A bipartite graph is constructed with volumes as the vertices and their feature similarities as edge weights. The maximum matches in this bipartite graph provide the volume correspondence between two videos. The highlighted edges represent the correspondences. Mean of the correspondence values gives the final matching score between the two videos.
A snapshot of our dataset that includes 337 videos comprising of 74 boat, 80 car, 148 airplanes, and 35 tank videos. The significant variation in the object appearance within each category makes this dataset very challenging.
Video Matching for Retrieval
Video retrieval result. (a) Frames of the input query video. (b) A comparison of performance against two other approaches which are based on keyframes. The proposed volume based (blue) method produces higher precision values. (c) Keyframes of the top 10 similar videos out of the 100 retrieved videos.
Choosing the right features
This figure presents the effects of different combinations of features on the quality of retrieval for the query video. Several combinations can be chosen based on different weights of these features. It was found that the best performance is achieved using a combination of all the volume based features.
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