Places clustering of full-length film key-framesusing latent aspect modeling over SIFT matches

Authors:
Maguelonne Héritier;Langis Gagnon;Samuel Foucher
Affiliations:
Research and Development Department, Computer Research Institute of Montréal, Montréal, QC, Canada;Research and Development Department, Computer Research Institute of Montréal, Montréal, QC, Canada;Research and Development Department, Computer Research Institute of Montréal, Montréal, QC, Canada
Venue:
IEEE Transactions on Circuits and Systems for Video Technology
Year:
2009

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Abstract

An improved unsupervised classification method to extract and link places features and cluster recurrent physical locations (key-places) within a movie is presented. Our approach finds links between key frames of a common key-place based on the use of a probabilistic latent space model over the possible local matches between the key frames image set. This allows the extraction of significant groups of local matching descriptors that may represent characteristic elements of a key-place. An exhaustive evaluation of our approach was conducted on inhouse and public image datasets, as well as on full-length movies. Results revealed that our method is very efficient for near-duplicate objec/background detection with weak overlap. Performance measurements on full-length movies indicate a recognition rate of about 75% on the key-places clustering with a false alarm rate (FAR) of approximately 2%.