首页> 外文期刊>Procedia Computer Science >The key frame extraction algorithm based on the indigenous disturbance variation difference video
【24h】

The key frame extraction algorithm based on the indigenous disturbance variation difference video

机译:基于土着扰动变化差差的关键帧提取算法

获取原文
           

摘要

In view of the traditional support vector machine (SVM) learning algorithm for widespread learning parameters is not easy determined in the process of video key frame extraction, the problem of low accuracy, an independent perturbation variable difference SVM algorithm used for video key frame extraction. First of all, the study of the biological mechanism of differential evolution algorithm, put forward an improved way of autonomic disturbance variation. Secondly, combined with improved forms of independent disturbance differential evolution algorithm for SVM parameters optimization, designed the key frame extraction algorithm of the video based on the improved differential of SVM algorithm. Through the standard test functions and video test database experiments show that the improvement of autonomic disturbance variation difference video key frame extraction algorithm can more effectively optimize parameters of support vector machine, so as to contribute to the improvement of the recall of video retrieval rate of (quasi) two algorithms performance evaluation standard.
机译:鉴于传统的支持向量机(SVM)学习算法对于广泛的学习参数,在视频键帧提取过程中不容易确定,精度低的问题,用于视频键帧提取的独立扰动可变差异SVM算法。首先,研究差分进化算法的生物机制,提出了一种改进的自主扰动变化方式。其次,与SVM参数优化的改进形式的独立扰动差分演化算法,基于SVM算法改进差分设计了视频的关键帧提取算法。通过标准测试功能和视频测试数据库实验表明,自主扰动变化差视频键框架提取算法的改进可以更有效地优化支持向量机的参数,从而有助于改善视频检索率的调查( Quasi)两种算法性能评估标准。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号