Implementasi Metode SVM untuk Klasifikasi Bunga dengan Ekstraksi Fitur Histogram of Gradient (HOG)

Abstract

Feature extraction techniques are applied to obtain features that will be useful in classifying and recognizing images. Feature extraction techniques are very helpful in various image processing applications. Many studies show the effectiveness of feature extraction before classification. There is also research showing that a feature extraction method may be good for certain classification approaches but may also be preferable. The impact of the sample and its size can also determine the results of the application of feature extraction techniques in the classification process. In this study, the author aims to prove the effectiveness of the application of the HOG feature extraction technique on the classification with the SVM method on flower images. The experiment was carried out on two groups of images, where the first group was image classes with relatively uniform colors and shapes, both in shape and color. and the second group is image classes with relatively different colors and shapes in the same class. The results showed that image datasets with relatively uniform colors and shapes do not require the application of any feature extraction to produce high accuracy. For classification by performing feature extraction in this study it gives different results for the two interest groups.