Enhancing Bag-of-Words Models by Efficient Semantics-Preserving Metric Learning
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.
Bag-of-words models, distance metric learning, image annotation, multimedia and graphics, object codebook, object recognition, semantic gap
Databases and Information Systems | Theory and Algorithms
Data Management and Analytics
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2308