Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial ...Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.展开更多
In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weake...In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weaken weight is proposed in the paper firstly.Combining with the object center weight based on the kernel function,the problem of interference of the similar color background can be solved.And then,a model updating strategy is presented to improve the tracking robustness on the influence of occlusion,illumination,deformation and so on.With the test on the sequence of Tiger,the proposed approach provides better performance than the original mean shift tracking algorithm.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
AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. F...AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. First, we compute the RGB color variance to evaluate the quality of the input image. If this variance is very small, we extract near-neutral color areas and compute the local ab-chromaticity histogram. We use this local ab-chromaticity histogram to evaluate the quality of the input image. This method has been tested in ZTE＇ s video surveil- lance system. The results show that the proposed method pro- duces better results based on subjective evaluation and is more efficient in various conditions.展开更多
A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems （CBIR）. The color feature is described by the color histogram ...A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems （CBIR）. The color feature is described by the color histogram （CH）, which is translation and rotation invariant. However, the CH does not contain spatial information which is very important for the image retrieval. To overcome this shortcoming, the subband energy of the lifting directionlet transform （L-DT） is proposed to describe the directional information, in which L-DT is characterized by multi-direction and anisotropic basis functions compared with the wavelet transform. A global similarity measure is designed to implement the fusion of both color feature and anisotropic directionality for the retrieval process. The retrieval experiments using a set of COREL images demonstrate that the higher query precision and better visual effect can be achieved.展开更多
In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact t...In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.展开更多
The perfect image retrieval and retrieval time are the two major challenges inCBIR systems. To improve the retrieval accuracy, the whole database is searched basedon many image characteristics such as color, shape, te...The perfect image retrieval and retrieval time are the two major challenges inCBIR systems. To improve the retrieval accuracy, the whole database is searched basedon many image characteristics such as color, shape, texture and edge information whichleads to more time consumption. This paper presents a new fuzzy based CBIR method,which utilizes colour, shape and texture attributes of the image. Fuzzy rule based systemis developed by combining color, shape, and texture feature for enhanced image recovery.In this approach, DWT is used to pull out the texture characteristics and the region basedmoment invariant is utilized to pull out the shape features of an image. Color similarityand texture attributes are extorted using customized Color Difference Histogram (CDH).The performance evaluation based on precision and BEP measures reveals the superiorityof the proposed method over renowned obtainable approaches.展开更多
The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the charact...The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD（Earth Mover’s Distance） distance and texture distance. EMD distance is the distance between the histograms of two images in HSV（Hue, Saturation, Value） color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.展开更多
An evolutionary programming based algorithm was proposed for color image quantization. A novel hybrid mutation operator was disigned to improve the quantization quality, and a stochastic sampling scheme was also prese...An evolutionary programming based algorithm was proposed for color image quantization. A novel hybrid mutation operator was disigned to improve the quantization quality, and a stochastic sampling scheme was also presented for saving the run time. The experimental results demonstrate the superior performance of the proposed algorithm in comparison with the GA based algorithm.展开更多
An efficient method using various histogram-based(high-dimension al)im age con tent descriptors for automatically classifying general color photos into relevant cate gories is pre-sent ed.Principal component analysis(...An efficient method using various histogram-based(high-dimension al)im age con tent descriptors for automatically classifying general color photos into relevant cate gories is pre-sent ed.Principal component analysis(PCA)is used to project the original high dimensional his tograms onto their eigenspaces.Lower dimensional eigenfeatures are then used to train sup-port vector machines(SVMs )to classify images into their cate gories.Experimen tal results show that even though different descriptors perform differently,they are all highly redundant.It is shown that the dimension ality of all these descriptors,re gard less of their performances,can be signifi cant ly reduced without affecting classification accura cy.Such scheme would be useful when it is used in an interactive setting for relevant feedback in con tent -based image re-trieval,where low dimen sional content descriptors will enable fast on line learn ing and re clas-sification of results.展开更多
为了解决单相机平台受高分辨率与大视场角之间矛盾制约的问题,本文提出了一种大视场、高分辨率、无色差的复眼相机阵列系统。结合人眼视觉成像理论,建立非均匀采样的仿生视觉模型,降低系统输入端的数据冗余;设计了一种多图像平滑加权融...为了解决单相机平台受高分辨率与大视场角之间矛盾制约的问题,本文提出了一种大视场、高分辨率、无色差的复眼相机阵列系统。结合人眼视觉成像理论,建立非均匀采样的仿生视觉模型,降低系统输入端的数据冗余;设计了一种多图像平滑加权融合算法,实现大视场图像无缝拼接;提出了一种基于多级迭代直方图匹配的色彩校正算法,提高了不同子相机间的色彩一致性;利用相邻子相机间重叠区域,对感兴趣区域(Region of Interest,ROI)进行多图像超分辨率重建。最终搭建了曲面相机阵列系统,实现了无色差大视场高分辨率图像的获取。实验结果表明,该系统能够达到单个子相机9倍的视场角与分辨率,并实现了ROI的4倍超分辨率重建。相比于典型的色彩校正算法HM,本文所提方法的PSNR和HSM指标分别提升了4.1%和15.9%,满足了多相机系统在复杂场景下的大视场、高分辨率、无色差的需求。展开更多
回环检测是消除同时定位与地图构建(simultaneous localization and mapping,SLAM)系统中累计误差的关键所在,在光照条件或视角变化较大的情况下,传统的基于外观的回环检测方法往往失效。针对这种情况,在ORBSLAM2的框架基础上提出一种...回环检测是消除同时定位与地图构建(simultaneous localization and mapping,SLAM)系统中累计误差的关键所在,在光照条件或视角变化较大的情况下,传统的基于外观的回环检测方法往往失效。针对这种情况,在ORBSLAM2的框架基础上提出一种物体级的回环检测方法。利用目标检测获得的语义信息和特征点信息构建物体级语义地图。将语义地图抽象成拓扑图并将地标抽象成节点,用颜色直方图描述节点信息,结合节点间的几何关系,基于语义和几何一致性约束,提出一种图匹配方法实现回环检测。当检测到回环时,通过物体对齐的方式进行回环校正。在公开的TUM和USTC数据集上进行实验,结果表明提出的系统精度较ORBSLAM2平均提高了49.58%,并且构建的语义地图显示出良好的定位效果。展开更多
基金supported by the MOE(Ministry of Education of China)Project of Humanities and Social Sciences(23YJAZH169)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T2020017)Henan Foreign Experts Project No.HNGD2023027.
文摘Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.
基金National Natural Science Foundation of China(No.61201412)
文摘In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weaken weight is proposed in the paper firstly.Combining with the object center weight based on the kernel function,the problem of interference of the similar color background can be solved.And then,a model updating strategy is presented to improve the tracking robustness on the influence of occlusion,illumination,deformation and so on.With the test on the sequence of Tiger,the proposed approach provides better performance than the original mean shift tracking algorithm.
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.
文摘AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. First, we compute the RGB color variance to evaluate the quality of the input image. If this variance is very small, we extract near-neutral color areas and compute the local ab-chromaticity histogram. We use this local ab-chromaticity histogram to evaluate the quality of the input image. This method has been tested in ZTE＇ s video surveil- lance system. The results show that the proposed method pro- duces better results based on subjective evaluation and is more efficient in various conditions.
基金supported by the National High Technology Research and Development Program of China (863 Program) (2007AA12Z1362007AA12Z223)+2 种基金the National Basic Research Program of China (973Program) (2006CB705707)the National Natural Science Foundation of China (60672126, 60607010)the Program for Cheung Kong Scholars and Innovative Research Team in University (IRT0645)
文摘A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems （CBIR）. The color feature is described by the color histogram （CH）, which is translation and rotation invariant. However, the CH does not contain spatial information which is very important for the image retrieval. To overcome this shortcoming, the subband energy of the lifting directionlet transform （L-DT） is proposed to describe the directional information, in which L-DT is characterized by multi-direction and anisotropic basis functions compared with the wavelet transform. A global similarity measure is designed to implement the fusion of both color feature and anisotropic directionality for the retrieval process. The retrieval experiments using a set of COREL images demonstrate that the higher query precision and better visual effect can be achieved.
文摘In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.
文摘The perfect image retrieval and retrieval time are the two major challenges inCBIR systems. To improve the retrieval accuracy, the whole database is searched basedon many image characteristics such as color, shape, texture and edge information whichleads to more time consumption. This paper presents a new fuzzy based CBIR method,which utilizes colour, shape and texture attributes of the image. Fuzzy rule based systemis developed by combining color, shape, and texture feature for enhanced image recovery.In this approach, DWT is used to pull out the texture characteristics and the region basedmoment invariant is utilized to pull out the shape features of an image. Color similarityand texture attributes are extorted using customized Color Difference Histogram (CDH).The performance evaluation based on precision and BEP measures reveals the superiorityof the proposed method over renowned obtainable approaches.
文摘The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD（Earth Mover’s Distance） distance and texture distance. EMD distance is the distance between the histograms of two images in HSV（Hue, Saturation, Value） color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.
文摘An evolutionary programming based algorithm was proposed for color image quantization. A novel hybrid mutation operator was disigned to improve the quantization quality, and a stochastic sampling scheme was also presented for saving the run time. The experimental results demonstrate the superior performance of the proposed algorithm in comparison with the GA based algorithm.
文摘An efficient method using various histogram-based(high-dimension al)im age con tent descriptors for automatically classifying general color photos into relevant cate gories is pre-sent ed.Principal component analysis(PCA)is used to project the original high dimensional his tograms onto their eigenspaces.Lower dimensional eigenfeatures are then used to train sup-port vector machines(SVMs )to classify images into their cate gories.Experimen tal results show that even though different descriptors perform differently,they are all highly redundant.It is shown that the dimension ality of all these descriptors,re gard less of their performances,can be signifi cant ly reduced without affecting classification accura cy.Such scheme would be useful when it is used in an interactive setting for relevant feedback in con tent -based image re-trieval,where low dimen sional content descriptors will enable fast on line learn ing and re clas-sification of results.
文摘为了解决单相机平台受高分辨率与大视场角之间矛盾制约的问题,本文提出了一种大视场、高分辨率、无色差的复眼相机阵列系统。结合人眼视觉成像理论,建立非均匀采样的仿生视觉模型,降低系统输入端的数据冗余;设计了一种多图像平滑加权融合算法,实现大视场图像无缝拼接;提出了一种基于多级迭代直方图匹配的色彩校正算法,提高了不同子相机间的色彩一致性;利用相邻子相机间重叠区域,对感兴趣区域(Region of Interest,ROI)进行多图像超分辨率重建。最终搭建了曲面相机阵列系统,实现了无色差大视场高分辨率图像的获取。实验结果表明,该系统能够达到单个子相机9倍的视场角与分辨率,并实现了ROI的4倍超分辨率重建。相比于典型的色彩校正算法HM,本文所提方法的PSNR和HSM指标分别提升了4.1%和15.9%,满足了多相机系统在复杂场景下的大视场、高分辨率、无色差的需求。
文摘回环检测是消除同时定位与地图构建(simultaneous localization and mapping,SLAM)系统中累计误差的关键所在,在光照条件或视角变化较大的情况下,传统的基于外观的回环检测方法往往失效。针对这种情况,在ORBSLAM2的框架基础上提出一种物体级的回环检测方法。利用目标检测获得的语义信息和特征点信息构建物体级语义地图。将语义地图抽象成拓扑图并将地标抽象成节点,用颜色直方图描述节点信息,结合节点间的几何关系,基于语义和几何一致性约束,提出一种图匹配方法实现回环检测。当检测到回环时,通过物体对齐的方式进行回环校正。在公开的TUM和USTC数据集上进行实验,结果表明提出的系统精度较ORBSLAM2平均提高了49.58%,并且构建的语义地图显示出良好的定位效果。
