Video Summarization Using Fuzzy C-Means Clustering

The rapid growth of digital world and computer networking are contributing to an enormous and continuous growing of video content. Despite the greatly growth in digital videotechnologies, the capabilities of users to manipulate, interact with and manage videos are still far behind what users canachieve with other types of media such as text or images. This is primarily because of temporal and multi-modal nature of video and the size of the associated medium. Between research topics,video summarization is an important one that improves faster browsing of large video collections and also more efficient contentindexing and access. We also introduce a new keyframe extraction system that produces static video summaries, usingfuzzy c-means clustering. We choose frame with maximum membership grade for any clusters as keyframe. Number of clusters estimated with a simple method. The summaries that produced by users are used for evaluation. These summaries are compared both to our approach and also to a number ofother techniques in the literature. Experimental results show that the proposed solution provided static video summaries withmore relevance with original video and user’s intention. Also our method is considerable that gives high accuracy with low error rate

Keywords:

Video summarization , Key frame extraction , Fuzzy C-Means , Clustering