Batch color classification using bag of colors and discriminative sparse coding

M. Soltani-Sarvestani, Azimifar Zohreh
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引用次数: 3

Abstract

Color can be a useful feature in many fields of AI that are based on machine vision. Unfortunately, many existing vision system do not use color to its full extent, largely because color-based recognition in outdoor scene is complicated, and existing color machine vision techniques have not been shown to be effective in realistic outdoor images. The problem of color recognition in outdoor is considerable when we are faced with glossy materials like automobiles. There is no powerful method to recognize color of a batch of pixels. Thus, for the first time, we propose a novel method to detect dominant color of a group of pixels. This method has many applications in object color detection especially for glossy objects.
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采用颜色包和判别稀疏编码进行批量颜色分类
在许多基于机器视觉的人工智能领域,颜色是一个有用的特征。遗憾的是,许多现有的视觉系统并没有充分利用颜色,这很大程度上是因为基于颜色的户外场景识别是复杂的,现有的颜色机器视觉技术并没有显示出对真实的户外图像的有效性。当我们面对像汽车这样有光泽的材料时,户外的色彩识别问题是相当大的。目前还没有一种有效的方法来识别一批像素的颜色。因此,我们首次提出了一种新的方法来检测一组像素的主色。该方法在物体颜色检测中有着广泛的应用,特别是对光滑物体的颜色检测。
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