Convolutional Neural Networks for Clothes Categories

Clothes classification is a promising research topic. Due to the manually-designed features' limitation, the existing algorithms have a problem of low accuracy in attributes classification. In this paper, we propose a new method to utilize convolutional deep learning for clothes classification. We firstly set up a new database by downloading the images of each category from Internet via related software and manual work, which divides clothes into 16 categories according to the common clothing style in the market. Then, the paper designs convolutional neural networks(CNNs) architecture and adaptively learns the feature representation of clothes from our constructed dataset. The experiment adopts Bag ofWords (BOW), Histogram of Oriented Gradient (HOG)+Support Vector Machine(SVM) and HSV (Hue, Saturation, Value)+SVM to test the new database and compares these methods with our CNNs model. The results demonstrate the superiority of our CNNs to the other algorithms.