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Boosting color saliency in image feature detection

机译:在图像特征检测中提高色彩显着性

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The aim of salient feature detection is to find distinctive local events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape-saliency of the local neighborhood. The majority of these detectors are luminance-based, which has the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. In this paper, color distinctiveness is explicitly incorporated into the design of saliency detection. The algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features.
机译:显着特征检测的目的是在图像中找到独特的局部事件。显着特征通常由图像的局部微分结构确定。他们专注于当地社区的形状显着性。这些检测器中的大多数是基于亮度的,其缺点是在确定显着图像特征时完全忽略了局部颜色信息的独特性。为了充分利用彩色图像中显着点检测的可能性,除了形状特征外,还应考虑颜色特征。在本文中,显着性检测的设计中明确地包含了颜色唯一性。该算法称为色彩显着性增强,它基于对彩色图像导数的统计数据的分析。显色性增强设计为一种通用方法,可轻松适应现有的特征检测器。结果表明,通过针对彩色显着特征,可以大大改善信息内容。

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