Abstract
Support Vector Machine (SVM) with Radial Basis Functions (RBF) kernel is one of the methods frequently applied to nonlinear multiclass image classification. To overcome some constraints in the form of a large number of image datasets divided into nonlinear multiclass, there three stages of SVM-RBF classification process carried out i.e. 1) Determining the algorithms of feature extraction and feature value dimensions used, 2) Determining the appropriate kernel and parameter values, and 3) Using correct multiclass method for the training and testing processes. The OaO, OaA, and DAGSVM multi-class methods were tested on a large dataset of batik motif images whose geometric motifs with a variety of patterns and colors in each class and containing similar patterns in the motifs between the classes. DAGSVM has the advantage in classification accuracy value, i.e. 91%, but it takes longer during the training and testing processes.
Highlights
Studies focusing on classification of image recognition have a high level of complexity if it has a large dataset with many different groups or multiple classes
The results showed that the classification accuracy value using Support Vector Machine (SVM) was better than by using Minimum Distance Method and Backpropagation Neural Network [6, 11]
The non-linear multiclass classification optimization method with SVM that uses Large Datasets of Geometric Motif Image can be explained in detail as follows: 1) The feature creation phase is used to optimize the classification accuracy results with feature extraction using Discrete Wavelet Transformation (DWT)
Summary
Studies focusing on classification of image recognition have a high level of complexity if it has a large dataset with many different groups or multiple classes. There are several supervised Machine Learning algorithms which can be used for image recognition classification, such as hierarchical-based decision trees, K-Nearest Neighborhood algorithms, partitionbased K-means and minimum-distance, and networks based Artificial Neural Network (ANN) like perceptron algorithm, Backpropagation Neural Network (BNN), and Support Vector Machine (SVM). These classification algorithms are categorized as Shallow Learning type since they still require some application of feature extraction algorithms to produce feature image dataset. Feature extraction is a fundamental part in classification as feature dataset obtained from proper feature extraction can maximize the accuracy value of classification results [1,2]
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