New fusional framework combining sparse selection and clustering for key frame extraction. Issue 4 (26th April 2016)

• Record Type: Journal Article
• Authors: Fei, Mengjuan; Jiang, Wei; Mao, Weijie; Song, Zhendong
• Is Part Of: IET computer vision. Volume 10:Issue 4(2016)
• Page Start: 280
• Page End: 288
• Publication Date: 2016-04-26
• Subjects: video signal processing -- video retrieval -- indexing -- pattern clustering; fusional framework -- sparse selection -- video indexing -- video content preservation -- mutual information based agglomerative hierarchical clustering -- optimal key frame -- content loss minimisation -- candidate key frames -- syncretic key frame extraction; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0237
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Key frame extraction can facilitate rapid browsing and efficient video indexing in many applications. However, to be effective, key frames must preserve sufficient video content while also being compact and representative. This study proposes a syncretic key frame extraction framework that combines sparse selection (SS) and mutual information‐based agglomerative hierarchical clustering (MIAHC) to generate effective video summaries. In the proposed framework, the SS algorithm is first applied to the original video sequences to obtain optimal key frames. Then, using content‐loss minimisation and representativeness ranking, several candidate key frames are efficiently selected and grouped as initial clusters. A post‐processor – an improved MIAHC – subsequently performs further processing to eliminate redundant images and generate the final key frames. The proposed framework overcomes issues such as information redundancy and computational complexity that afflict conventional SS methods by first obtaining candidate key frames instead of accurate key frames. Subsequently, application of the improved MIAHC to these candidate key frames rather than the original video not only results in the generation of accurate key frames, but also reduces the computation time for clustering large videos. The results of comparative experiments conducted on two benchmark datasets verify that the performance of the proposed SS–MIAHC framework is superior to that of conventional methods.