Extended Bayesian generalization model for understanding user’s intention in semantics based images retrieval

Extended Bayesian generalization model for understanding user’s intention in semantics based... Learning concepts from examples presented in user’s query and infer the other items that belong to this query is still a significant challenge for images retrieval systems. Existing models from cognitive science namely Bayesian models of generalization mainly focus on this challenge where they remarkably succeed at explaining how to generalize from few examples in a wide range of domains. However their success largely depends on the validity of examples. They require that each example is a good representative, which is not always the case in the context of images retrieval. In this paper, we will extend the Bayesian models of generalization to identify the appropriate level of generalization for a given query in the context of query by semantic example systems. Our model uses an ontology as the basis of its hypothesis space which allows us to take advantages of its semantic richness and inference capacity. Experimental study using the ImageNet benchmark verifies the efficiency of our model in comparison to the state-of-the-art models of generalization. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Extended Bayesian generalization model for understanding user’s intention in semantics based images retrieval

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6205-0
Publisher site
See Article on Publisher Site

Abstract

Learning concepts from examples presented in user’s query and infer the other items that belong to this query is still a significant challenge for images retrieval systems. Existing models from cognitive science namely Bayesian models of generalization mainly focus on this challenge where they remarkably succeed at explaining how to generalize from few examples in a wide range of domains. However their success largely depends on the validity of examples. They require that each example is a good representative, which is not always the case in the context of images retrieval. In this paper, we will extend the Bayesian models of generalization to identify the appropriate level of generalization for a given query in the context of query by semantic example systems. Our model uses an ontology as the basis of its hypothesis space which allows us to take advantages of its semantic richness and inference capacity. Experimental study using the ImageNet benchmark verifies the efficiency of our model in comparison to the state-of-the-art models of generalization.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 4, 2018

References

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