Keyframe-based video summarization using Delaunay clustering

By Padmavathi Mundur, Yong Rao, Y. Yesha. International Journal on Digital Libraries, 2006

338 citations recorded by the source corpus.

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Video Summarization with Long Short-Term Memory (2016, methodology): “In this paper, we focus on the two most common ones: keyframe selection, where the system identifies a series of defining frames [1,2,3,4,5] and key subshot selection, where the system identifies a series of defining subshots, each of which is a temporally contiguous set of frames spanning a short t”
Diverse Sequential Subset Selection for Supervised Video Summarization (2014, methodology): “Unsupervised methods Supervised subset selection DT STIMO VSUMM1 VSUMM2 DPP + Q/D Ours (seqDPP+) [30] [31] [24] [24] [14] Q/D LINEAR N.”
Unsupervised Video Summarization with Adversarial LSTM Networks (2017, neutral): “[23] - - 57.”
Deep Attentive Video Summarization With Distribution Consistency Learning (2020, background): “Accordingly, many efforts are devoted by employing and designing various clustering methods, ranging from k-means [21], Delaunay clustering [22], graph clustering [23], and prototype selection [4], to archetypal analysis [13].”
A Survey on Visual Content-Based Video Indexing and Retrieval (2011, background): “In recent years, many approaches [182], [189] have been”
VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method (2011, methodology): “Several authors have used this approach (Gong and Liu, 2000; Yahiaoui et al., 2001; Mundur et al., 2006; Wang and Merialdo, 2009; Furini et al., 2010), and it is also used in this work.”