用于监视对象检索的底层特征的最佳组合

Proceedings of the International Conference on Signal Processing and Multimedia Applications Pub Date : 2011-07-18 DOI:10.5220/0003527101870192

Virginia Fernandez Arguedas, K. Chandramouli, Qianni Zhang, E. Izquierdo

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引用次数: 1

摘要

本文提出了一种基于低层次多特征融合的分类器，用于研究监控视频中目标检索方法的性能。提出的检索框架利用了基于生物启发优化技术的进化计算算法的最新发展。多描述符空间由四个MPEG-7视觉特征组合而成。针对AVSS 2007数据集中提取的对象，对所提出的方法进行了核机评估。

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Optimal combination of low-level features for surveillance object retrieval

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.

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Proceedings of the International Conference on Signal Processing and Multimedia Applications

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