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Scene Understanding in Images

机译:在图像中的场景理解

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摘要

Scene understanding targets on the automatic identification of thoughts, opinion, emotions, and sentiment of the scene with polarity. The sole aim of scene understanding is to build a system which infer and understand the image or a video just like how humans do. In the paper, we propose two algorithms- Eigenfaces and Bezier Curve based algorithms for scene understanding in images. The work focuses on a group of people and thus, targets to perceive the sentiment of the group. The proposed algorithm consist of three different phases. In the first phase, face detection is performed. In the second phase, sentiment of each person in the image is identified and are combined to identify the overall sentiment in the third phase. Experimental results show Bezier curve approach gives better performance than Eigenfaces approach in recognizing the sentiments in multiple faces.
机译:现场了解思想,意见,情绪的自动识别目标,具有极性的思想,意见,情感和情绪。场景理解的唯一目的是建立一个系统推断和理解图像或视频的系统,就像人类一样。在论文中,我们提出了两种算法基于算法和基于Bezier曲线的图像算法。这项工作侧重于一群人,因此,目标是察觉本集团的情绪。所提出的算法包括三个不同的阶段。在第一阶段中,执行面部检测。在第二阶段中,识别图像中的每个人的情绪,并组合以识别第三阶段的整体情绪。实验结果表明,Bezier曲线方法提供比识别多个面纱情绪的更好的性能。

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