人工鱼群优化最小二乘支持向量机的视频字幕定位

征选择是视频字幕定位的关键，为了提高视频字幕定位正确率，提出一种人工鱼群算法(AFSA)和最小二乘支持向量机(LSSVM)相融合的视频字幕定位模型(AFSA-LSSVM)。首先提取视频字幕特征，然后通过模拟鱼群的觅食、聚群及追尾行为找到最优视频字幕特征子集，最后采用LSSVM建立最优视频字幕定位模型，并进行仿真对比实验。结果表明，相对其它视频字幕定位模型，AFSA-LSSVM提高了视频字幕定位正确率和效率，可为后续视频内容的安全分析提供技术支持.

Video captions location based on artificial fish swarm algorithm and least square support vector machine

Feature selection is a key problem in video caption location, in order to improve location rate of the video caption, a video caption location model (AFSA-LSSVM) is proposed in this paper which integrates artificial fish swarm algorithm (AFSA) with least squares support vector machine (LSSVM). Firstly, video caption features are extracted, and then he optimal feature subset are found by simulating feeding, clustering and the following behavior of fish warm, finally, LSSVM is used to establish the optimal video caption location model, and the simulation experiment is carried out to test the performance of model. The results show that, compared with other video caption location models, the proposed model has improved location rate and efficiency of video caption location and, which can provide technical support for following video content security analysis.