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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.
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.
Super-Resolution Reconstruction based on Tukey Norm and Adaptive Bilateral Total Variation
Jie Shen,Feng Xu,Mengxi Xu,Yun Yang,Ruili Wang,Lili Zhang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.5
In Bilateral Total Variation (BTV) regularized super-resolution reconstruction (SRR), the fidelity item is only applicable to a specific noise model, and the fixed weight of BTV regularization term cannot adapt to the changes in an image. Thus, this paper proposes a SRR algorithm based on the Tukey fidelity term and adaptive BTV regularization term. The Tukey fidelity term has a more effective outliers suppression feature to deal with complex noises, and the weight of adaptive BTV regularization term can resize itself according to the changes of image textures, which can achieve the purposes of suppressing noises and preserving edges. Experimental results show that, compared with other algorithms, the proposed algorithm has better vision effects and higher Peak Signal-to-noise Ratio (PSNR) values.