基于特征对比度的显著性检测方法在处理复杂背景图像时,容易将对比度较高的背景区域误判为目标,导致分割结果不准确.为解决上述问题,提出了一种基于改进凸包和全局颜色对比度的彩色图像分割方法.首先以超像素为基本计算单元,根据图像颜色及其空间分布,计算基于颜色对比度的基本显著图;然后利用颜色增强Harris角点得到目标的凸包,并利用FH方法生成的超像素对凸包进行修正,以此为基础计算基于改进凸包的中心显著图;最后将上述2个显著图进行加权融合得到最终显著图,并使用大津法得到图像中的目标.通过在MSRA1000和ECSSD数据集上进行实验,结果表明本文算法相较于其他算法在可视效果和准确率、召回率等评价指标方面有明显的优势.
The detection method based on feature contrast is easy to misjudge the background area with high contrast as the target when processing complex background images. This may lead to inaccurate segmentation results. In order to solve this problem, this paper presents a new method for color image segmentation based on improved convex hull and color contrast. Firstly, the method takes superpixel as the basic processing unit, and measures the saliency map based on color contrast with uniqueness and spatial distribution. Secondly, it calculates convex hull using color enhancement Harris corner points. The convex hull is corrected through FH algorithm. The center saliency map can be achieved through the corrected convex hull. The final saliency map is obtained by the fusion of the contrast map and the center saliency map. Finally, the object was segmented out using OTSU method. Simulation experiments on the MSRA 1 000 and ECSSD databases were conducted. The results indicate that the proposed algorithm performs better in visualization, precision.
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