Video summarization using a network of radial basis functions

The exponential increase of video data on the internet demands efficient management schemes for storage, retrieval, and indexing. One of the methods for managing this huge volume of video data is video summarization. Video summarization is a method to generate smart versions of the videos for efficient retrieval and browsing on the internet. Key frame extraction is a type of video summarization in which the video contents are represented by salient frames of the video. Most of the applications of key frames are user-centered whereby the key frames are used to assist human users in video browsing. However, most of the existing techniques for extracting key frames do not encompass users’ feedback in the retrieval of key frames. In this paper, we propose a user-centered scheme for extracting key frames. In our scheme, the system parameters are learned at training time in the light of users’ feedback. For successful modeling of human perception of similarity in k-means clustering, a non-linear model based on a network of Radial basis functions is employed to reduce the semantic gap between index features and human perception. Experimental results show that the proposed scheme gives excellent results as compared to some of the other techniques.