基于语义容忍的大型图像/视频检索图像表示

Ying Dai
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引用次数: 5

摘要

在许多领域中,关于多媒体的概念的性质是不精确的,在人类感知的层面上,对发现类似媒体的解释也是模糊和主观的。为了解决这些问题,本文系统地定义了用于表示视频片段的图像或关键帧的语义类别,以及类别之间的容错程度,提出了语义类之间容错关系的建模方法。此外,为了消除使用语义容差关系模型产生的伪容差,在图像/关键帧表示中引入了不容差方法。另一方面,描述了基于语义容差的图像/视频自动表示图,提出了基于图像/视频语义表示的大型图像/视频检索结构。我们将所提出的方法应用于自然与人造领域、人类与非人类领域和时间领域的图像表示,并展示了使用和不使用语义容忍关系模型的分类结果。此外,本文提出的大型图像/视频数据的语义表示和检索机制与现有方法进行了比较。实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Tolerance-Based Image Representation for Large Image/Video Retrieval
The nature of the concepts regarding multimedia in many domains is imprecise, and the interpretation of finding similar media is also ambiguous and subjective on the level of human perception. To solve these problems, in this paper, semantic categories of images or key frames which are extracted for representing the segments of a video, and the tolerance degree between the categories are defined systematically, and the approach of modeling tolerance relations between the semantic classes is proposed. Furthermore, for removing the induced false tolerance in the produce of using semantic tolerance relation model, the method of un-tolerating is introduced in image/key frame representation. On the other hand, a diagram of semantic tolerance-based image/video automatic representation is described, and the structure of large image/video retrieval using image/video semantic representation is proposed. We apply the proposed approach to the representations of images regarding the nature vs. man-made domain, human vs. non-human domain, and temporal domain, and show the categorization results of using and not using semantic tolerance relation model. Furthermore, the mechanism of the semantic representation and retrieval for large image/video data proposed in this paper is compared with the state-of-the-art methods. The results show the effectiveness of proposed method.
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