DocumentCode
679772
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.
fYear
2011
fDate
18-21 July 2011
Firstpage
1
Lastpage
6
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing and Multimedia Applications (SIGMAP), 2011 Proceedings of the International Conference on
Conference_Location
Seville, Spain
Type
conf
Filename
6731299
Link To Document