Fashion Apparel Recognition by Objectness Measurement and Deep Convolutional Network
Date Issued
2015
Date
2015
Author(s)
Hou, Yu-Lin
Abstract
Recently, with the emerging of online shopping platforms, analyzing clothing attributes on user-generated photos becomes an important key for the related applications nowadays. In this paper, we introduce a system framework for recognizing fashion apparel (i.e., clothing categories), which is the crucial part of clothing attributes. We integrate the datasets with large variety collected from different platforms and utilize the powerfulness of Deep Convolutional Neural Network which has been shown the capability of adapting to various domains and tasks. Previous works mainly use the part-based methods (e.g., body part detector, pose estimator) as alignment to locate the clothing-related regions for recognizing clothing. However, this constraints the problem on human centric photos. In this work, our proposed method removes the constraint of human body existence which is generally used for alignment and learns with image-level annotated data to recognize multiple clothing garments in real-world data.
Subjects
Deep learning
Fully convolutional network
clothing recognition
multi-label prediction
automatical annotation
Type
thesis
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