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Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification
Xu, Mengxi,Sun, Quansen,Lu, Yingshu,Shen, Chenming The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.
Nearest-Neighbors Based Weighted Method for the BOVW Applied to Image Classification
Mengxi Xu,Quansen Sun,Yingshu Lu,Chenming Shen 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.4
This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.
Macro-Micro Mechanical Behavior of Crushable Granular Materials under Generalized Stress Conditions
Yiming Liu,Chenming Xu,Guofang Xu,Haijun Mao,Zunqun Xiao 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.5
This paper investigated the joint influence of particle crushing and intermediate principal stress coefficient on the macro and micro performances of granular assembles by using 3D discrete element method. The soil particle was modeled by crushable agglomerate. Numerical true triaxial tests with crushable agglomerates were performed. The mechanical behaviors of specimens with unbreakable agglomerates were also tested in this study. The results demonstrated that grain breakage can significantly affect the behaviors of granular soils in both macro and micro scales. Moreover, the present paper also showed that particle breakage did not influence the unique relationship between the intermediate principal stress coefficient band the deviatoric fabric of strong contacts. This can provide a good basis for developing micro-parameter-based constitutive model considering particle breakage for granular soils.