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SIFT match verification by geometric coding for large-scale partial-duplicate web image search

SIFT match verification by geometric coding for large-scale partial-duplicate web image search SIFT Match Verification by Geometric Coding for Large-Scale Partial-Duplicate Web Image Search WENGANG ZHOU and HOUQIANG LI, University of Science and Technology of China YIJUAN LU, Texas State University QI TIAN, University of Texas at San Antonio Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visualwords model does not capture the geometric context among local features in images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for largescale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

SIFT match verification by geometric coding for large-scale partial-duplicate web image search

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/2422956.2422960
Publisher site
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Abstract

SIFT Match Verification by Geometric Coding for Large-Scale Partial-Duplicate Web Image Search WENGANG ZHOU and HOUQIANG LI, University of Science and Technology of China YIJUAN LU, Texas State University QI TIAN, University of Texas at San Antonio Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visualwords model does not capture the geometric context among local features in images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for largescale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion,

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: Feb 1, 2013

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