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Contribution of Color Information in Visual Saliency Model for Videos

机译：视频视觉显着模型中的颜色信息的贡献

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Much research has been concerned with the contribution of the low level features of a visual scene to the deployment of visual attention. Bottom-up saliency models have been developed to predict the location of gaze according to these features. So far, color besides to brightness, contrast and motion is considered as one of the primary features in computing bottom-up saliency. However, its contribution in guiding eye movements when viewing natural scenes has been debated. We investigated the contribution of color information in a bottom-up visual saliency model. The model efficiency was tested using the experimental data obtained on 45 observers who were eye tracked while freely exploring a large data set of color and grayscale videos. The two datasets of recorded eye positions, for grayscale and color videos, were compared with a luminance-based saliency model [1]. We incorporated chrominance information to the model. Results show that color information improves the performance of the saliency model in predicting eye positions.
机译：很多研究都关注了视觉场景的低级功能对视觉关注的贡献。已经开发了自下而上的显着模型来预测根据这些特征的凝视的位置。到目前为止，除了亮度，对比度和运动之外的颜色被认为是计算自下而下显着性的主要特征之一。然而，在观看自然场景时，它对引导眼影的贡献已经辩论。我们调查了彩色信息在自下而上的视觉显着模型中的贡献。使用45个观察者获得的实验数据测试了模型效率，该实验数据是在自由探索大型数据集的颜色和灰度视频的同时跟踪。与灰度和彩色视频的两个数据集与基于亮度的显着模型进行比较[1]。我们将色度信​​息纳入模型。结果表明，颜色信息提高了显着模型在预测眼睛位置的性能。

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