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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 video technologies, the capabilities of users to manipulate, interact with and manage videos are still far behind what users can achieve 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 content 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 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 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.