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

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 algor...

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Bibliographic Details
Main Authors: WU, Lei, HOI, Steven C. H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
Subjects:
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
Online Access:https://ink.library.smu.edu.sg/sis_research/2308
https://ink.library.smu.edu.sg/context/sis_research/article/3308/viewcontent/05720676.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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