Title :
Optimal combination of low-level features for surveillance object retrieval
Author :
Arguedas, Virginia Fernandez ; Chandramouli, Krishna ; Zhang, Qianni ; Izquierdo, Ebroul
Author_Institution :
Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science Queen Mary, University of London, Mile End Road, London, E1 4NS, U.K.
Abstract :
In this paper, a low-level multi-feature fusion based classifier is presented for studying the performance of an object retrieval method from surveillance videos. The proposed retrieval framework exploits the recent developments in evolutionary computation algorithm based on biologically inspired optimisation techniques. The multi-descriptor space is formed with a combination of four MPEG-7 visual features. The proposed approach has been evaluated against kernel machines for objects extracted from AVSS 2007 dataset.
Keywords :
Feature extraction; Image color analysis; Optimization; Surveillance; Training; Videos; Visualization; MPEG-7 features; Machine learning; Multi-feature fusion; Object retrieval; Particle swarm optimisation; Surveillance videos;
Conference_Titel :
Signal Processing and Multimedia Applications (SIGMAP), 2011 Proceedings of the International Conference on
Conference_Location :
Seville, Spain
