What is the best way for extracting meaningful attributes from pictures?

Liu, Liangchen, Wiliem, Arnold, Chen, Shaokang and Lovell, Brian C. (2016) What is the best way for extracting meaningful attributes from pictures?. Pattern Recognition, 64 314-326. doi:10.1016/j.patcog.2016.10.034

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ412250_OA.pdf Full text (open access) application/pdf 1.57MB 0

Author Liu, Liangchen
Wiliem, Arnold
Chen, Shaokang
Lovell, Brian C.
Title What is the best way for extracting meaningful attributes from pictures?
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
Publication date 2016-11-04
Sub-type Article (original research)
DOI 10.1016/j.patcog.2016.10.034
Open Access Status File (Author Post-print)
Volume 64
Start page 314
End page 326
Total pages 33
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract Automatic attribute discovery methods have gained in popularity to extract sets of visual attributes from images or videos for various tasks. Despite their good performance in some classification tasks, it is difficult to evaluate whether the attributes discovered by these methods are meaningful and which methods are the most appropriate to discover attributes for visual descriptions. In its simplest form, such an evaluation can be performed by manually verifying whether there is any consistent identifiable visual concept distinguishing between positive and negative exemplars labelled by an attribute. This manual checking is tedious, expensive and labour intensive. In addition, comparisons between different methods could also be problematic as it is not clear how one could quantitatively decide which attribute is more meaningful than the others. In this paper, we propose a novel attribute meaningfulness metric to address this challenging problem. With this metric, automatic quantitative evaluation can be performed on the attribute sets; thus, reducing the enormous effort to perform manual evaluation. The proposed metric is applied to some recent automatic attribute discovery and hashing methods on four attribute-labelled datasets. To further validate the efficacy of the proposed method, we conducted a user study. In addition, we also compared our metric with a semi-supervised attribute discover method using the mixture of probabilistic PCA. In our evaluation, we gleaned several insights that could be beneficial in developing new automatic attribute discovery methods.
Keyword Visual attribute
Meaningfulness metric
Attribute discovering
Semantic content
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 4 November 2016. Accepted manuscript

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 23 Nov 2016, 02:54:21 EST by Arnold Wiliem on behalf of School of Information Technol and Elec Engineering