Large Margin Discriminant Hashing for Fast k-Nearest Neighbor Classification

2010 ◽  
Author(s):  
Tomoyuki Shibata ◽  
Susumu Kubota ◽  
Satoshi Ito
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Large Margin ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Exclusive lasso-based k-nearest-neighbor classification

2021 ◽  
Author(s):  
Lin Qiu ◽  
Yanpeng Qu ◽  
Changjing Shang ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)

2013 ◽  
Vol 3 ◽  
pp. 462-469 ◽  
Author(s):  
Martijn D. Steenwijk ◽  
Petra J.W. Pouwels ◽  
Marita Daams ◽  
Jan Willem van Dalen ◽  
Matthan W.A. Caan ◽  
...  
Keyword(s):  
White Matter ◽  
Nearest Neighbor ◽  
White Matter Lesion ◽  
Tissue Type ◽  
Lesion Segmentation ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

2019 ◽  
Vol 1 (3) ◽  
pp. 1-12
Author(s):  
Agus Wahyu Widodo ◽  
Deo Hernando ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Mangrove Forest ◽  
Nearest Neighbor ◽  
Classification Method ◽  
Mangrove Forests ◽  
K Nearest Neighbor ◽  
Distance Method ◽  
K Value ◽  
Nearest Neighbor Classification ◽  
Haralick Features ◽  
Neighbor Classification

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

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