Table of Contents
ISRN Robotics
Volume 2013, Article ID 630579, 17 pages
Research Article

Classification of Clothing Using Midlevel Layers

Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA

Received 31 December 2012; Accepted 26 January 2013

Academic Editors: K. K. Ahn, L. Asplund, and R. Safaric

Copyright © 2013 Bryan Willimon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a multilayer approach to classify articles of clothing within a pile of laundry. The classification features are composed of color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The contribution of this paper is a novel approach of classification termed L-M-H, more specifically LC-S-H for clothing classification. The multilayer approach compartmentalizes the problem into a high (H) layer, multiple midlevel (characteristics (C), selection masks (S)) layers, and a low (L) layer. This approach produces “local” solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories (Willimon et al., 2011), +17.90% for four categories, and +10.35% for seven categories over the baseline system, using SVMs (Chang and Lin, 2001).