  The recognition of human activities is one of the key problems in video
understanding. Action recognition is challenging even for specific categories
of videos, such as sports, that contain only a small set of actions.
Interestingly, sports videos are accompanied by detailed commentaries available
online, which could be used to perform action annotation in a weakly-supervised
setting. For the specific case of Cricket videos, we address the challenge of
temporal segmentation and annotation of ctions with semantic descriptions. Our
solution consists of two stages. In the first stage, the video is segmented
into "scenes", by utilizing the scene category information extracted from
text-commentary. The second stage consists of classifying video-shots as well
as the phrases in the textual description into various categories. The relevant
phrases are then suitably mapped to the video-shots. The novel aspect of this
work is the fine temporal scale at which semantic information is assigned to
the video. As a result of our approach, we enable retrieval of specific actions
that last only a few seconds, from several hours of video. This solution yields
a large number of labeled exemplars, with no manual effort, that could be used
by machine learning algorithms to learn complex actions.

Multimedia;Computation and Language;Computer Vision and Pattern Recognition