Design and evaluation of classifier for identifying sign language videos in video sharing sites
Additional Document Info
Video sharing sites provide an opportunity for the collection and use of sign language presentations about a wide range of topics. Currently, locating sign language videos SL videosin such sharing sites relies on the existence and accuracy of tags, titles or other metadata indicating the content is in sign language. In this paper, we describe the design and evaluation of a classifier for distinguishing between sign language videos and other videos. A test collection of SL videos and videos likely to be incorrectly recognized as SL videos likely false positiveswas created for evaluating alternative classifiers. Five video features thought to be potentially valuable for this task were developed based on common video analysis techniques. A comparison of the relative value of the five video features shows that a measure of the symmetry of movement relative to the face is the best feature for distinguishing sign language videos. Overall, an SVM classifier provided with all five features achieves 82% precision and 90% recall when tested on the challenging test collection. The performance would be considerably higher when applied to the more varied collections of large video sharing sites.
name of conference
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility