Fashion 10000: an enriched social image dataset for fashion and clothing

Authors:
Babak Loni;Lei Yen Cheung;Michael Riegler;Alessandro Bozzon;Luke Gottlieb;Martha Larson
Affiliations:
Delft University of Technology, Netherlands;Delft University of Technology, Netherlands;Delft University of Technology, Netherlands;Delft University of Technology, Netherlands;International Computer Science Institute, Berkeley, CA;Delft University of Technology, Netherlands
Venue:
Proceedings of the 5th ACM Multimedia Systems Conference
Year:
2014

Citing 6
Cited 0

Games with a Purpose

Computer
The MIR flickr retrieval evaluation

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation

Proceedings of the international conference on Multimedia information retrieval
Parsing clothing in fashion photographs

CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Hi, magic closet, tell me what to wear!

Proceedings of the 20th ACM international conference on Multimedia
Fashion-focused creative commons social dataset

Proceedings of the 4th ACM Multimedia Systems Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we present a new social image dataset related to the fashion and clothing domain. The dataset contains more than 32000 images, their context and social metadata. Furthermore the dataset is enriched with several types of annotations collected from the Amazon Mechanical Turk (AMT) crowdsourcing platform, which can serve as ground truth for various content analysis algorithms. This dataset has been successfully used at the Crowdsourcing task of the 2013 MediaEval Multimedia Benchmarking initiative. The dataset contributes to several research areas such as Crowdsourcing, multimedia content and context analysis as well as hybrid human/automatic approaches. In this paper, the dataset is described in detail and the dataset collection strategy, statistics, applications of dataset and its contribution to MediaEval 2013 is discussed.