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- W3208587582 abstract "We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similarity path graph (SPG) G, represented by graph Laplacian matrix L, where the similarities between adjacent frames are encoded as positive edge weights. We show that maximizing the smallest eigenvalue λ <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>min</inf> (B) of a coefficient matrix B = diag(a) + µL, where a is the binary keyframe selection vector, is equivalent to minimizing a worst-case signal reconstruction error. We prove that, after partitioning $mathcal{G}$ into Q sub-graphs $left{ {{mathcal{G}^q}} right}_{q = 1}^Q$, the smallest Gershgorin circle theorem (GCT) lower bound of Q corresponding coefficient matrices—${min _q}lambda _{min }^ - left( {{{mathbf{B}}^q}} right)$—is a lower bound for λ <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>min</inf> (B). This inspires a fast graph sampling algorithm to iteratively partition $mathcal{G}$ into Q sub-graphs using Q samples (keyframes), while maximizing $lambda _{min }^ - left( {{{mathbf{B}}^q}} right)$ for each sub-graph ${mathcal{G}^q}$. Experimental results show that our algorithm achieves comparable video summarization performance as state-of-the-art methods, at a substantially reduced complexity." @default.
- W3208587582 created "2021-11-08" @default.
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- W3208587582 date "2022-05-23" @default.
- W3208587582 modified "2023-10-18" @default.
- W3208587582 title "Fast Graph Sampling for Short Video Summarization Using Gershgorin Disc Alignment" @default.
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- W3208587582 doi "https://doi.org/10.1109/icassp43922.2022.9746208" @default.
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