Conspicuity-based visual scene semantic similarity computing for video

Wei Wei, Tianyun Yan, Yuan-Mao Zhang
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Abstract

Based on saliency region representation of visual scene, a framework for quantifying the semantic similarity of two video scenes is proposed in this paper. Frame-segment key-frame strategy is used to concisely represent video content in temporal domain. Spatio-temporal conspicuity model for basic visual semantics, a neuromorphic model that simulates human visual system, is used to select dynamic and static spatial salient areas. With pattern classification technique, the basic visual semantics are recognized. Then, the similarity of two visual scenes is calculated according to information theoretic similarity principles and Tversky's set-theoretic similarity. Experiment results demonstrate the framework could compute quantitative semantic similarity of two video scenes.
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基于显著性的视频视觉场景语义相似度计算
基于视觉场景的显著区域表示,提出了一种量化两个视频场景语义相似度的框架。采用帧段关键帧策略,在时域内简洁地表示视频内容。基本视觉语义时空显著性模型是一种模拟人类视觉系统的神经形态模型,用于选择动态和静态空间显著区。利用模式分类技术,识别基本的视觉语义。然后,根据信息论相似原理和Tversky集合论相似度计算两个视觉场景的相似度。实验结果表明,该框架可以定量计算两个视频场景的语义相似度。
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