The Study of Fashion Style Classification: Harajuku-type Kawaii and Street Fashion

This work provides a comparison among four fashion
image classification models, which are a random forest
classification method, a convolutional neural network (CNN), a
CNN model trained with preprocessed images that a human head
and face are removed, and a CNN model trained with images that
a head, face, and background are removed. We are interested in
three fashion categories, including Harajuku kawaii style, Thai
street style, and European street fashion styles. We retrieved
images from Google Images, and adopted various preprocessing
techniques, which were human detection, clothing segmentation,
landmark detection, and colorfulness matrix to locate humans
and clothes, and extract fashion features. We grouped the images
into 2 classes, which were Harajuku kawaii fashion and other
street fashion styles. Results showed that the accuracy score from
the CNN model without cropping and background removing
process was greater than the others. However, when we validated
the models using the fashion dataset that was evaluated by fashion
experts, the result indicated that the performance of the CNN
model trained with the head-cropped image dataset was greater
than the others. We concluded that the deep learning model
trained with the head-cropped dataset achieved better results
because it learned only clothing features, which are the most
significant information in the fashion domain.
Index Terms—Image Classification, Kawaii Fashion, Street
Fashion, Deep Learning, Convolutional Neural Network