Adaptive key frame extraction using unsupervised clustering

TLDR

Key frame extraction is a critical problem in video information retrieval, yet existing methods are either computationally expensive or fail to capture salient visual content. The authors aim to overcome these limitations by proposing a new unsupervised clustering algorithm for key frame extraction. The algorithm is computationally simple and adapts to visual content. Its efficiency and effectiveness were validated on large amounts of real‑world videos.

Abstract

Key frame extraction has been recognized as one of the important research issues in video information retrieval. Although progress has been made in key frame extraction, the existing approaches are either computationally expensive or ineffective in capturing salient visual content. We first discuss the importance of key frame selection; and then review and evaluate the existing approaches. To overcome the shortcomings of the existing approaches, we introduce a new algorithm for key frame extraction based on unsupervised clustering. The proposed algorithm is both computationally simple and able to adapt to the visual content. The efficiency and effectiveness are validated by large amount of real-world videos.