Optimal combination of low-level features for surveillance object retrieval

摘要

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.