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- W2092095384 abstract "Visual Saliency of a video sequence can be computed by combining spatial and temporal features thatattract a userâs attention to a group of pixels. We present a method that computes video saliency byintegrating these features: color dissimilarity, objectness measure, motion difference, and boundary score.We use temporal clusters of pixels, or temporal superpixels, to simulate attention associated with a group ofmoving pixels in a video sequence. The features are combined using weights learned by a linear supportvector machine in an online fashion. The temporal linkage for superpixels is then used to find the saliencyflow across the image frames. We experimentally demonstrate the efficacy of the proposed method and thatthe method has better performance when compared to state-of-the-art methods." @default.
- W2092095384 created "2016-06-24" @default.
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- W2092095384 date "2015-01-01" @default.
- W2092095384 modified "2023-10-18" @default.
- W2092095384 title "Learning to Predict Video Saliency using Temporal Superpixels" @default.
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- W2092095384 doi "https://doi.org/10.5220/0005206402010209" @default.
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