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ID 34797
file
creator
Matsukawa, Tetsu
subject
Image recognition
Higher-order local autocorrelation feature
Bag-of-features
Posterior probability image
NDC
Information science
abstract
This paper presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of posterior probabilities on a posterior probability image is maximum. However, by using local autocorrelations of posterior probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results reveal that the proposed method exhibits higher classification performances than the standard bag-of-features method.
journal title
Pattern Recognition
volume
Volume 45
issue
Issue 2
start page
707
end page
719
date of issued
2012
publisher
Elsevier Science BV
issn
0031-3203
ncid
publisher doi
language
eng
nii type
Journal Article
HU type
Journal Articles
DCMI type
text
format
application/pdf
text version
author
rights
This is a preprint of an article submitted for consideration in Pattern Recognition (c) 2012 Elsevier B.V. ; Pattern Recognition is available online at ScienceDirect with the open URL of your article;
relation url
department
Graduate School of Engineering