UM  > 科技學院  > 電腦及資訊科學系
Feature guided biased gaussian mixture model for image matching
Kun Sun1; Peiran Li1; Wenbing Tao1; Yuanyan Tang2
2015-02-20
Source PublicationInformation Sciences
ISSN0020-0255
Volume295Pages:323-336
Abstract

In this article we propose a Feature Guided Biased Gaussian Mixture Model (FGBG) for image matching. We formulate the matching task as a Maximum a Posteriori (MAP) problem by seeing one point set as the centroid of a Gaussian Mixture Model (GMM) and the other point set as the data. A Thin Plate Spline (TPS) transformation between the two point sets is learnt so that the GMM can best fit the data. Our main contribution is to assign each Gaussian mixture component a different weight. This is where our model differs from the traditional Self Governed Balanced Gaussian Mixture Model (SGBG), whose Gaussian mixture components have equal coefficients. The new weight is defined as a value related to feature similarity, which can be computed by simply decomposing a distance matrix in the feature space. In this way, both feature similarity and spatial arrangement are considered. The feature descriptor is introduced as a reasonable prior to guide the matching, and the spatial transformation offers a global constraint so that local ambiguity can be alleviated. We solve this MAP problem in a framework similar to [16], in which Deterministic Annealing and the Expectation Maximization (EM) algorithms are used. We show that our FGBG algorithm is robust to outliers, deformation and rotation. Extensive experiments on self-collected and the latest open access data sets show that FGBG can boost the number of correct matches.

KeywordAnnealing Biased Deterministic Em Feature Gmm Guided Image Matching Tps
DOIhttps://doi.org/10.1016/j.ins.2014.10.029
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000346543000018
PublisherELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
Fulltext Access
Citation statistics
Cited Times [WOS]:16   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorWenbing Tao
Affiliation1.National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
2.Department of Computer and Information Science, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Kun Sun,Peiran Li,Wenbing Tao,et al. Feature guided biased gaussian mixture model for image matching[J]. Information Sciences,2015,295:323-336.
APA Kun Sun,Peiran Li,Wenbing Tao,&Yuanyan Tang.(2015).Feature guided biased gaussian mixture model for image matching.Information Sciences,295,323-336.
MLA Kun Sun,et al."Feature guided biased gaussian mixture model for image matching".Information Sciences 295(2015):323-336.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Kun Sun]'s Articles
[Peiran Li]'s Articles
[Wenbing Tao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Kun Sun]'s Articles
[Peiran Li]'s Articles
[Wenbing Tao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Kun Sun]'s Articles
[Peiran Li]'s Articles
[Wenbing Tao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.