针对原始的简单线性迭代聚类(simple linear iterative clustering,SLIC)超像素分割算法,在运行前需要根据经验来预设分割的超像素块数,这可能导致出现过分割或欠分割的问题。本文提出了一种改进的SLIC超像素分割算法,利用全局HSV颜色...针对原始的简单线性迭代聚类(simple linear iterative clustering,SLIC)超像素分割算法,在运行前需要根据经验来预设分割的超像素块数,这可能导致出现过分割或欠分割的问题。本文提出了一种改进的SLIC超像素分割算法,利用全局HSV颜色空间的非均匀量化来间接地表示待分割图像的复杂度,并进一步用其一维向量对应直方图的均值来表示预分割超像素的块数,从而达到自适应设置超像素块数的目的。随后为了获得更加完整地分割图像目标轮廓信息,本文还提出了利用图像的梯度信息对分割结果做进一步处理。最终结果显示,相较于原始的SLIC超像素分割算法,本文所提的改进SLIC算法能够在保障分割质量的前提下,大幅减少分割图像中的过割行为并保留目标图像的边界区域。展开更多
Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color...Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color space put more emphasis on color than texture feature;2)the methods extract several features respectively and combine them into a vector,in which bad features may lead to worse performance after combining directly good and bad features.To address the problems above,a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision.The bag-of-visual words(BoW)models and color intensity-based local difference patterns(CILDP)are exploited to capture local and global features of an image.The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method.The performance of our proposed framework in terms of average precision on Corel-1K database is86.26%,and it improves the average precision by approximately6.68%and12.53%over CILDP and BoW,respectively.Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.展开更多
文摘针对原始的简单线性迭代聚类(simple linear iterative clustering,SLIC)超像素分割算法,在运行前需要根据经验来预设分割的超像素块数,这可能导致出现过分割或欠分割的问题。本文提出了一种改进的SLIC超像素分割算法,利用全局HSV颜色空间的非均匀量化来间接地表示待分割图像的复杂度,并进一步用其一维向量对应直方图的均值来表示预分割超像素的块数,从而达到自适应设置超像素块数的目的。随后为了获得更加完整地分割图像目标轮廓信息,本文还提出了利用图像的梯度信息对分割结果做进一步处理。最终结果显示,相较于原始的SLIC超像素分割算法,本文所提的改进SLIC算法能够在保障分割质量的前提下,大幅减少分割图像中的过割行为并保留目标图像的边界区域。
基金Projects(61370200,61672130,61602082) supported by the National Natural Science Foundation of ChinaProject(1721203049-1) supported by the Science and Technology Research and Development Plan Project of Handan,Hebei Province,China
文摘Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color space put more emphasis on color than texture feature;2)the methods extract several features respectively and combine them into a vector,in which bad features may lead to worse performance after combining directly good and bad features.To address the problems above,a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision.The bag-of-visual words(BoW)models and color intensity-based local difference patterns(CILDP)are exploited to capture local and global features of an image.The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method.The performance of our proposed framework in terms of average precision on Corel-1K database is86.26%,and it improves the average precision by approximately6.68%and12.53%over CILDP and BoW,respectively.Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.
