A content-based video retrieval system: video retrieval with extensive features

The existing video retrieval techniques are inefficient because they utilise a specific feature. In this paper, a proficient system with extensive features is proposed for improving the retrieval efficiency. The system segments a video into shots, and then a few representative frames are extracted from each shot, and colour, contour, texture and motion features-based frame descriptors are computed for these shots and stored in a feature library. When a query clip is given, the aforesaid features are extracted for the query clip and compared with the features that are already stored in the feature library. Latent semantic indexing (LSI)-based similarity measure is used for performing the comparison. Finally, videos that are similar to the query are retrieved from the enormous collection of videos. The proposed system is evaluated with precision-recall and F score and they are compared with existing retrieval systems.

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