LSIS TREC VIDEO 2009 High Level Feature Retrieval using Compact Profile Entropy Descriptors
Hervé Glotin, Zhong‐Qiu Zhao, Emilie Dumont · 2009
Abstract. 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.