改进流形排序算法的绿色柑橘识别

Improved Manifold Ranking Algorithm for Green Citrus Recognition

针对自然环境下绿色柑橘与背景具有相似的颜色特征导致柑橘难以识别的问题,提出一种改进流形排序算法的显著性检测方法。首先,为了避免绿色柑橘图像亮度不均匀导致识别难度增加,利用基于模糊集理论的亮度增强方法对柑橘图像进行预处理。其次,为了解决传统的基于图的流形排序显著性检测算法依赖边界背景先验获取前景种子,导致显著图效果不理想的问题,使用相对总变差和局部复杂度结合的方法提取更准确的前景种子。最后,将提取到的前景种子与去除前景种子的边界背景先验显著图相结合进行流形排序,得到最后的显著图。实验结果表明,所提算法能更有效地识别出绿色柑橘区域,分割准确率、假阳性率和假阴性率达94%、3.19%和1.64%。

This research proposes a saliency detecting approach based on an improved manifold ranking algorithm,aiming at the problem that green citrus has similar color features to the background in the natural environment,making the citrus difficult to be recognized.First,to avoid the increasing difficulty of recognizing caused by the uneven brightness of the green citrus images,the brightness improvement approach based on fuzzy set theory was employed to preprocess the orange images.Second,to resolve the issue that the traditional graphbased manifold ranking saliency detection algorithm relies on the boundary background to obtain the foreground seeds,resulting in the unsatisfactory effect of the saliency map,an approach combining relative total variation and local complexity was employed to extract more accurate foreground seeds.Finally,to sort the manifolds,the extracted foreground seeds were combined with the a priori saliency map of the boundary background without foreground seeds and the final saliency map was obtained.Experimental findings indicate that the proposed algorithm can recognize the green citrus region more effectively,and the segmentation accuracy,falsepositive rate,and falsenegative rate are 94%,3.19%,and 1.64%,respectively.