颜色是基于内容的图像检索的重要特征.传统颜色直方图由于只考虑色彩总量而无法区别色彩空间分布差异.本文提出了一种新的颜色密度直方图(Color Density Histogram CDH).通过计算主要颜色的密度,反映颜色的空间分布离散程度.密度大,颜...颜色是基于内容的图像检索的重要特征.传统颜色直方图由于只考虑色彩总量而无法区别色彩空间分布差异.本文提出了一种新的颜色密度直方图(Color Density Histogram CDH).通过计算主要颜色的密度,反映颜色的空间分布离散程度.密度大,颜色分布较集中,密度小,则颜色分布较分散.在HSV颜色空间上,使用CAREL的1000图像做测试数据集,在平均查准率、查全率上都优于颜色直方图方法.结果表明,CDH能够结合颜色和空间特征,比传统的颜色直方图具有具有更好的检索效果.展开更多
This paper introduces the principles of using color histogram to match images in CBIR. And a prototype CBIR system is designed with color matching function. A new method using 2-dimensional color histogram based on hu...This paper introduces the principles of using color histogram to match images in CBIR. And a prototype CBIR system is designed with color matching function. A new method using 2-dimensional color histogram based on hue and saturation to extract and represent color information of an image is presented. We also improve the Euclidean-distance algorithm by adding Center of Color to it. The experiment shows modifications made to Euclidean-distance signif-icantly elevate the quality and efficiency of retrieval.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
文摘颜色是基于内容的图像检索的重要特征.传统颜色直方图由于只考虑色彩总量而无法区别色彩空间分布差异.本文提出了一种新的颜色密度直方图(Color Density Histogram CDH).通过计算主要颜色的密度,反映颜色的空间分布离散程度.密度大,颜色分布较集中,密度小,则颜色分布较分散.在HSV颜色空间上,使用CAREL的1000图像做测试数据集,在平均查准率、查全率上都优于颜色直方图方法.结果表明,CDH能够结合颜色和空间特征,比传统的颜色直方图具有具有更好的检索效果.
基金Supported by the Project of Science & Technology Depart ment of Shanghai (No.055115001)
文摘This paper introduces the principles of using color histogram to match images in CBIR. And a prototype CBIR system is designed with color matching function. A new method using 2-dimensional color histogram based on hue and saturation to extract and represent color information of an image is presented. We also improve the Euclidean-distance algorithm by adding Center of Color to it. The experiment shows modifications made to Euclidean-distance signif-icantly elevate the quality and efficiency of retrieval.
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.
