LSIS TREC VIDEO 2009 High Level Feature Retrieval using Compact Profile Entropy Descriptors
نویسندگان
چکیده
We build a fast video shot retrieval system in the context of the NIST TREC Video 2009 evaluation campaign. We compare our efficient Profile Entropy Features (PEF) to usual features, using various classifiers. These PEF are derived using the projection in the horizontal and vertical orientations. These features are then fed to SVM or KNNG classifiers to produce the keyframe ranks, from which we can get the shot ranks. The experimental results show that our PEF features outperform other features such as EDGE, GABOR, HSV, and so on. Moreover, PEF are very compact and fast to compute, and thus may be improved in further video retrieval systems.
منابع مشابه
LSIS TREC VIDEO 2008 High Level Feature Shot Segmentation using Compact Profile Entropy and Affinity Propagation Clustering
In this task, we build fast video indexing systems using a kind of efficient features based on the entropy of pixel projections. These features of 45 dimensions, called Profil Entropy Features (PEF), are derived using the projection in the horizontal orientation. These features are then fed to SVMs to produce the keyframe ranks, from which we can get the shot ranks. In the runs, we divided the ...
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