Fashion image classification on mobile phones using layered deep convolutional neural networks
Kazunori Hori, Shogo Okada, Katsumi Nitta · 2016
Toward implementation of fashion recommendation system based on photos taken with mobile phones, we propose a framework to recognize hierarchical categories of a fashion item. To classify an arbitrary photo of clothes robustly, (1) we collected two kind of dataset: (I) 120K datasets of clothes images on EC sites to train the classifier and (II) the clothing image set composed of photos taken by participants with their mobile phones. (2) we proposed Layered Deep Convolutional Neural Networks (LDCNNs) which is specialized in classifying images into hierarchical categories: hoodie is a lower in hierarchy in tops category. Experimental result shows proposed LDCNNs obtained mean accuracy of 92.7% for datasets from EC sites and 96.9% for those from mobile phones. This results are better than those (84.9%, 90.6 % respectively) for MLR+CNN in classification accuracy.