Abstract book (pdf) - ICPR 2010

Huang, Qingming, Chinese Acad. of Sciences
Jiang, Shuqiang, Chinese Acad. of Sciences
Tian, Qi, Univ. of Texas at San Antonio
Human action recognition has been well studied recently, but recognizing the activities of more than three persons remains
a challenging task. In this paper, we propose a motion trajectory based method to classify human group activities. Gaussian
Processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability
of people’s activities in group. With respect to the relationships of persons in group activities, three discriminative descriptors
are designed, which are Individual, Dual and Unitized Group Activity Pattern. We adopt the Bag of Words approach
to solve the problem of unbalanced number of persons in different activities. Experiments are conducted on the
human group-activity video database, and the results show that our approach outperforms the state-of-the-art.
13:30-16:30, Paper WeBCT9.27
Extracting Captions in Complex Background from Videos
Liu, Xiaoqian, Chinese Acad. of Sciences
Wang, Weiqiang, Chinese Acad. of Sciences
Captions in videos play a significant role for automatically understanding and indexing video content, since much semantic
information is associated with them. This paper presents an effective approach to extracting captions from videos, in which
multiple different categories of features (edge, color, stroke etc.) are utilized, and the spatio-temporal characteristics of
captions are considered. First, our method exploits the distribution of gradient directions to decompose a video into a sequence
of clips temporally, so that each clip contains a caption at most, which makes the successive extraction computation
more efficient and accurate. For each clip, the edge and corner information are then utilized to locate text regions. Further,
text pixels are extracted based on the assumption that text pixels in text regions always have homogeneous color, and their
quantity dominates the region relative to non-text pixels with different colors. Finally, the segmentation results are further
refined. The encouraging experimental results on 2565 characters have preliminarily validated our approach.
13:30-16:30, Paper WeBCT9.28
Keyframe-Guided Automatic Non-Linear Video Editing
Rajgopalan, Vaishnavi, Concordia Univ.
Ranganathan, Ananth, Honda Res. Inst. USA
Rajagopalan, Ramgopal, Res. in Motion
Mudur, Sudhir, Concordia Univ.
We describe a system for generating coherent movies from a collection of unedited videos. The generation process is
guided by one or more input keyframes, which determine the content of the generated video. The basic mechanism involves
similarity analysis using the histogram intersection function. The function is applied to spatial pyramid histograms computed
on the video frames in the collection using Dense SIFT features. A two-directional greedy path finding algorithm is
used to select and arrange frames from the collection while maintaining visual similarity, coherence, and continuity. Our
system demonstrates promising results on large video collections and is a first step towards increased automation in nonlinear
video editing.
13:30-16:30, Paper WeBCT9.29
Images in News
Sankaranarayanan, Jagan, Univ. of Maryland
Samet, Hanan, Univ. of Maryland
A system, called News Stand, is introduced that automatically extracts images from news articles. The system takes RSS feeds of news
article and applies an online clustering algorithm so that articles belonging to the same news topic can be associated with the same cluster.
Using the feature vector associated with the cluster, the images from news articles that form the cluster are extracted. First, the caption text
associated with each of the images embedded in the news article is determined. This is done by analyzing the structure of the news article’s
HTML page. If the caption and feature vector of the cluster are found to contain keywords in common, then the image is added to an image
repository. Additional meta-information are now associated with each image such as caption, cluster features, names of people in the news
article, etc. A very large repository containing more than 983k images from 12 million news articles was built using this approach. This
repository also contained more than 86.8 million keywords associated with the images. The key contribution of this work is that it combines
clustering and natural language processing tasks to automatically create a large corpus of news images with good quality tags or meta-information
so that interesting vision tasks can be performed on it.
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