Content Based Video Retrieval Using SURF Descriptor

S. Asha, M. Sreeraj
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引用次数: 18

Abstract

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.
基于内容的SURF描述符视频检索
提出了一种鲁棒的基于内容的视频检索(CBVR)系统。该系统基于一种称为SURF(加速鲁棒特征)的局部特征描述符来检索相似的视频。类SURF特征描述符的高维导致了视频信息索引过程中巨大的存储消耗。为了实现SURF特征描述符的降维,本系统采用随机降维方法,为视频提供模型数据。在检索时,使用最小距离分类器将测试片段的模型数据分类到相似的视频中。在检索阶段,使用两种不同的最小距离分类器对系统的性能进行了评估。实验分析表明,该系统的检索性能达到78%。本系统还分析了低维SURF描述符的性能效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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