Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2009

Abstract

This paper proposes a new approach for the discovery of common patterns in a small set of images by region matching. The issues in feature robustness, matching robustness and noise artifact are addressed to delve into the potential of using regions as the basic matching unit. We novelly employ the many-to-many (M2M) matching strategy, specifically with the Earth Mover's Distance (EMD), to increase resilience towards the structural inconsistency from improper region segmentation. However, the matching pattern of M2M is dispersed and unregulated in nature, leading to the challenges of mining a common pattern while identifying the underlying transformation. To avoid analysis on unregulated matching, we propose localized matching for the collaborative mining of common patterns from multiple images. The patterns are refined iteratively using the expectation-maximization algorithm by taking advantage of the "crowding" phenomenon in the EMD flows. Experimental results show that our approach can handle images with significant image noise and background clutter. To pinpoint the potential of Common Pattern Discovery (CPD), we further use image retrieval as an example to show the application of CPD for pattern learning in relevance feedback. (C) 2009 Elsevier B.V. All rights reserved.

Keywords

Common Pattern Discovery, Earth Mover's Distance, Localized matching, Local Flow Maximization, Expectation-maximization

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Image and Vision Computing

Volume

27

Issue

10

First Page

1470

Last Page

1483

ISSN

0262-8856

Identifier

10.1016/j.imavis.2009.01.002

Publisher

Elsevier

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1016/j.imavis.2009.01.002

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