With the advent and proliferation of low cost and high performance digital video recorder devices, an increasing
number of personal home video clips are recorded and stored by the consumers. Compared to image data, video
data is lager in size and richer in multimedia content. Efficient access to video content is expected to be more
challenging than image mining. Previously, we have developed a content-based image retrieval system and the
benchmarking framework for personal images. In this paper, we extend our personal image retrieval system to
include personal home video clips.
A possible initial solution to video mining is to represent video clips by a set of key frames extracted from
them thus converting the problem into an image search one. Here we report that a careful selection of key
frames may improve the retrieval accuracy. However, because video also has temporal dimension, its key frame
representation is inherently limited. The use of temporal information can give us better representation for video
content at semantic object and concept levels than image-only based representation.
In this paper we propose a bottom-up framework to combine interest point tracking, image segmentation and
motion-shape factorization to decompose the video into spatiotemporal regions. We show an example application
of activity concept detection using the trajectories extracted from the spatio-temporal regions. The proposed
approach shows good potential for concise representation and indexing of objects and their motion in real-life
consumer video.
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