Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2011

Abstract

An effective relevance feedback solution plays a key role in interactive intelligent 3D object retrieval systems. In this work, we investigate the relevance feedback problem for interactive intelligent 3D object retrieval, with the focus on studying effective machine learning algorithms for improving the user's interaction in the retrieval task. One of the key challenges is to learn appropriate kernel similarity measure between 3D objects through the relevance feedback interaction with users. We address this challenge by presenting a novel framework of Active multiple kernel learning (AMKL), which exploits multiple kernel learning techniques for relevance feedback in interactive 3D object retrieval. The proposed framework aims to efficiently identify an optimal combination of multiple kernels by asking the users to label the most informative 3D images. We evaluate the proposed techniques on a dataset of over 10, 000 3D models collected from the World Wide Web. Our experimental results show that the proposed AMKL technique is significantly more effective for 3D object retrieval than the regular relevance feedback techniques widely used in interactive contentbased image retrieval, and thus is promising for enhancing user's interaction in such interactive intelligent retrieval systems.

Keywords

Debugging, Machine learning, End-user programming

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Interactive Intelligent Systems

Volume

1

Issue

1

First Page

3-1

Last Page

27

ISSN

2160-6455

Identifier

10.1145/2030365.2030368

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2030365.2030368

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