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Introduction: Videos play an
ever increasing role in our everyday lives with applications
ranging from news, entertainment, scientific research, security
and surveillance. Coupled with the fact that cameras and storage
media are becoming less expensive, it has resulted in people
producing more video content than ever before. This necessitates
the development of efficient indexing and retrieval schemes for
video data. Most state-of-the-art techniques index videos
according to the global content in the scene such as color,
texture, brightness etc. There has been very little effort to
index videos according to the activity being performed in it. Our
research focuses on mining and indexing videos according to
activities in an unsupervised setting.
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