A Classification Method for “kawaii” Images Using Various Feature Filters

ABSTRACT In this paper, we propose a classification method for “kawaii” images. Conventional image classification methods, such as CNN (Convolutional Neural Network) and SVM (Support Vector Machine), can classify ordinary images, such as faces, with high accuracy, but they are insufficient for classifying images that are vaguely expressed without a “kawaii” explicit indicator. Through experiments, we propose a suitable method for classifying “kawaii” images by extracting latent color, shape, and other features of “kawaii” images using feature filters, quantitatively representing them, and then comparing classification accuracy using various classifiers based on machine learning techniques. In the experiments, the color, SIFT(Scale Invariant Feature Transform), and line features of the images are extracted using filters and compared using NN (Neural Network), Random Forest, AdaBoost, and SVM. The results show the effectiveness of the proposed feature filters and the suitability of Random Forest as a classifier, and thus an effective method for classification of “kawaii” images.