Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2011
Abstract
The authors present an online semantics preserving, metric learning technique for improving the bag-of-words model and addressing the semantic-gap issue. This article investigates the challenge of reducing the semantic gap for building BoW models for image representation; propose a novel OSPML algorithm for enhancing BoW by minimizing the semantic loss, which is efficient and scalable for enhancing BoW models for large-scale applications; apply the proposed technique for large-scale image annotation and object recognition; and compare it to the state of the art.
Keywords
Bag-of-words models, distance metric learning, image annotation, multimedia and graphics, object codebook, object recognition, semantic gap
Discipline
Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
IEEE Multimedia
Volume
18
Issue
1
First Page
24
Last Page
37
ISSN
1070-986X
Identifier
10.1109/MMUL.2011.7
Publisher
IEEE
Citation
WU, Lei and HOI, Steven C. H..
Enhancing Bag-of-Words Models by Efficient Semantics-Preserving Metric Learning. (2011). IEEE Multimedia. 18, (1), 24-37.
Available at: https://ink.library.smu.edu.sg/sis_research/2308
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/MMUL.2011.7