PT Unknown
AU Vacit Oguz Yazici
   Joost Van de Weijer
   Arnau Ramisa
TI Color Naming for Multi-Color Fashion Items
BT 6th World Conference on Information Systems and Technologies
PY 2018
BP 64
EP 73
VL 747
DE Deep learning; Color; Multi-label
AB There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results.
ER