Browsing by Subject "Particle swarm optimisation"

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    The COST292 experimental framework for TRECVID 2007
    (National Institute of Standards and Technology, 2007) Zhang, Q.; Corvaglia, M.; Aksoy, Selim; Naci, U.; Adami, N.; Aginako, N.; Alatan, A.; Alexandre, L. A.; Almeida, P.; Avrithis, Y.; Benois-Pineau, J.; Chandramouli, K.; Damnjanovic, U.; Esen, E.; Goya, J.; Grzegorzek, M.; Hanjalic, A.; Izquierdo, E.; Jarina, R.; Kapsalas, P.; Kompatsiaris, I.; Kuba, M.; Leonardi, R.; Makris, L.; Mansencal, B.; Mezaris, V.; Moumtzidou, A.; Mylonas, P.; Nikolopoulos, S.; Piatrik, T.; Pinheiro, A. M. G.; Reljin, B.; Spyrou, E.; Tolias, G.; Vrochidis, S.; Yakın, G.; Zajic, G.
    In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features.