20th Iranian Conference on Electrical Engineering, (ICEE2012), May 15-17, Tehran, Iran Video Summarization Using Fuzzy C-Means Clustering Ebrahim Asadi*, Nasrolla Moghadam Charkari** * Tarbiat Modares University , e.asadi@modares.ac.ir ** Tarbiat Modares University, moghadam@modares.ac.ir Abstract: The rapid growth of digital world and computer net working are contributing to an enormous and continuous grow ing of video content. Despite the greatly growth in digital video technologies. the capabilities of users to manipulate, interact with and manage videos are still far behind what users can achieve with other ypes 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 eicient con tent indexing and access. We also introduce a new keyframe extraction system that produces static video summaries, using fuzzy c-means clustering. We choose frame with mximum membership grade for any clusters as keyframe. Number of clusters estimated with a simple metho. The summaries that produced by users are used for evaluation. These summaries are compared both to our approach and also to a number of other techniques in the literature. Experimental results show that the proposed solution provided static video summaries with more 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 rame extraction, Fuzzy C-Means, Clustering. 1. Introduction Rapid development of computation, communications, and storage inrastructures, are contributing to an enor mous and steadily growing availability of video content. Despite the enormous investments in digital video tech nologies, the capabilities of an average user to manipu late, interact with and manage videos are still far behind what average users can achieve with other types of media such as text or images. This is mainly due to the temporal and multi-modal nature of video and the size of the asso ciated medium. To ind items of interest in this ocean of multimedia content, users have adopted services such as electronic program guides, TV web-portals, and web search engines that aggregate information relevant to the users' queries and allow them to ind easily the content they are looking for. 978-1-4673-1148-9112/$3l.00 ©2012 IEEE 690 However, while content offer and availability for the average users have increased enormously, ree time for consuming content has not increased much. The key problem of each consumer is to make eicient use of the ree-time available for enjoying content. Automatic video summarization aims at creating eicient representations of video for facilitating brows ing, search and, more generically, management of digital multimedia content. Automatically generated summaries can support users in navigating large video archives and in taking decisions more eiciently regarding selecting, consuming, sharing, or deleting content. Video summarization is a technic to produce a still or moving sequence of images rom original video as a summary for that video. There are two main video sum marization technics [1]: static video summarization (key rame extraction) and dynamic video summarization (video skimming). Static video summaries consist of a set of rames (keyrames) extracted rom the original video, while dynamic video summaries are a video clip consists of a collection of video segments (and corresponding audio) extracted rom the original video. Video skim include audio and motion that consist of more information. In addition, it is often more entertain ing and interesting to watch a skim than a slide show of keyframes [2]. On the other hand, keyrame sets are not restricted by any timing or synchronization issues and, therefore, they offer much more lexibility in terms of organization for browsing and navigation purposes, in comparison to strict sequential display of video skims [3- 6]. Also our proposed method produces a static video summary. Various approaches have been proposed in the litera ture, most of them based on clustering techniques [7-11]. The basic idea is clustering together similar rames/shots and then extraction some rames (generally one rame) per cluster as key rames. These methods are different in features (e.g., color histogram, luminance, and motion vector) and clustering algorithms (e.g., k-means, hierar chical).