A Image Matching Comparison Using the K-Nearest Neighbor (KNN) Method and Support Vector Machine (SVM)
Image matching is the process of finding digital images that have a degree of similarity. matching images using the classification method. In measuring image matching, the images used are original logo images and manipulated logo images. Comparison of classification algorithms from the two methods namely K-Nearest Neighbor (KNN) and Support Vector Machine with Sequential Minimal Optimization (SMO) optimization used to calculate matches based on accuracy values. The K-Nearest Neighbor (KNN) classification method is based on proximity or K calculations while the Support Vector Machine (SVM) classification method measures the distance between the hyperplane and the nearest data. Image match values are measured by Precision, Recall, F1-Score, and Accuracy. The image matching steps start from the preparation of data processing, extraction of HSV color features and shapes, then the classification stage. Digital images are used as many as 10 images consisting of one original logo and 9 manipulated logos. In the classification testing stage, using the WEKA application by applying the 10-fold cross-validation method. From the results of tests conducted that the closest k-neighbor (KNN) classification method is 80% and has a k = 0.889 which is quite good in measuring proximity, while the SVM classification method is 70%. The results of this image matching comparison can be concluded that the K-Nearest Neighbor classification method works better than SVM for image matching.
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