This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the origi...This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the original image,the proposed method first segments the input image into superpixels and,then,several different types of features are calculated according to these superpixels.To increase classification accuracy,the dimensions of these features are reduced using the principal component analysis(PCA)algorithm followed by a weighted serial feature fusion strategy.After constructing a coding dictionary using the nonnegative matrix factorization(NMF)algorithm,the input image is recognized by a support vector machine(SVM)model.The effectiveness of the proposed method was tested on the public Scene-15,Caltech-101,and Caltech-256 datasets,and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.展开更多
In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and dis...In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.展开更多
This paper presents a new kind of image retrieval system which obtains the feature vectors of im-ages by estimating their fraetal dimension; and at the same time establishes a tree-structure image database. After p...This paper presents a new kind of image retrieval system which obtains the feature vectors of im-ages by estimating their fraetal dimension; and at the same time establishes a tree-structure image database. After preproeessing and feature extracting, a given image is matched with the standard images in the image da-tabase using a hierarchical method of image indexing.展开更多
We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based...We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.展开更多
目的提出一种结合C/S(Client/Server)架构和BRF(Boosted random ferns)算法的移动增强现实应用方案,以保证图像识别算法对于产品外包装的识别性能。方法 BRF是一种高效、鲁棒的特征匹配算法,但由于手机内存及处理器等硬件条件的制约,不...目的提出一种结合C/S(Client/Server)架构和BRF(Boosted random ferns)算法的移动增强现实应用方案,以保证图像识别算法对于产品外包装的识别性能。方法 BRF是一种高效、鲁棒的特征匹配算法,但由于手机内存及处理器等硬件条件的制约,不能直接适用于手机终端。将C/S模式与BRF算法相结合应用于图像特征匹配,并设计实验测试比较文中方案(CS-BRF)与ORB算法的识别速度和匹配精度。结果实验结果表明,相比ORB算法,CS-BRF在识别速度相近的前提下,具有更为优异的识别精度。结论 CS-BRF能够实时准确识别印刷品图像,良好适用于产品包装移动增强现实系统。展开更多
基金the National Key Research and Development Program of China under Grant No.2018AAA0103203.
文摘This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the original image,the proposed method first segments the input image into superpixels and,then,several different types of features are calculated according to these superpixels.To increase classification accuracy,the dimensions of these features are reduced using the principal component analysis(PCA)algorithm followed by a weighted serial feature fusion strategy.After constructing a coding dictionary using the nonnegative matrix factorization(NMF)algorithm,the input image is recognized by a support vector machine(SVM)model.The effectiveness of the proposed method was tested on the public Scene-15,Caltech-101,and Caltech-256 datasets,and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.
文摘In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.
文摘This paper presents a new kind of image retrieval system which obtains the feature vectors of im-ages by estimating their fraetal dimension; and at the same time establishes a tree-structure image database. After preproeessing and feature extracting, a given image is matched with the standard images in the image da-tabase using a hierarchical method of image indexing.
文摘We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.
文摘目的提出一种结合C/S(Client/Server)架构和BRF(Boosted random ferns)算法的移动增强现实应用方案,以保证图像识别算法对于产品外包装的识别性能。方法 BRF是一种高效、鲁棒的特征匹配算法,但由于手机内存及处理器等硬件条件的制约,不能直接适用于手机终端。将C/S模式与BRF算法相结合应用于图像特征匹配,并设计实验测试比较文中方案(CS-BRF)与ORB算法的识别速度和匹配精度。结果实验结果表明,相比ORB算法,CS-BRF在识别速度相近的前提下,具有更为优异的识别精度。结论 CS-BRF能够实时准确识别印刷品图像,良好适用于产品包装移动增强现实系统。
