计算机工程2012，Vol.38Issue(23)：169-172,4.DOI:10.3969/j.issn.1000-3428.2012.23.042
基于改进K-means算法的关键帧提取
Key Frame Extraction Based on Improved K-means Algorithm
摘要
Abstract
In order to overcome the problems that the traditional clustering algorithm is sensitive to the initial parameter in the key frame extraction process, an efficient algorithm for key frame extraction based on improved K-means algorithm is proposed in this paper. In the implementation of the Artificial Fish Swarm Algorithm(AFSA) clustering algorithm, the artificial fish implement self-organization clustering under the guidance of group similarity and ultimately the artificial fish gathered in several extreme points, according to the greatest progress principle. The artificial fish with the biggest group similarity in each extreme point is set as the initialized cluster center. This paper implements K-means algorithm to obtain the final clustering result and extracts key frame. Experimental result shows that the accuracy of this algorithm is high, and can well express the main content of the video.关键词
视频检索/关键帧/群体相似度/特征提取/人工鱼群算法Key words
video retrieval/ key frame/ group similarity/ feature extraction/ Artificial Fish Swarm Algorithm(AFSA)分类
信息技术与安全科学引用本文复制引用
孙淑敏,张建明,孙春梅..基于改进K-means算法的关键帧提取[J].计算机工程,2012,38(23):169-172,4.基金项目
国家自然科学基金资助项目(61170126) （61170126）
江苏省自然科学基金资助项目(BK2009199) （BK2009199）
