Title

Enhancing Bag-of-Words Models by Efficient Semantics-Preserving Metric Learning

Publication Type

Journal Article

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 Management and Analytics

Publication

IEEE Multimedia

Volume

18

Issue

1

First Page

24

Last Page

37

ISSN

1070-986X

Identifier

10.1109/MMUL.2011.7

Publisher

IEEE

Additional URL

http://dx.doi.org/10.1109/MMUL.2011.7