CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor
This paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local inte...
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doaj-67f8a5aef5dd48ca80f2b9daf343abbb2021-09-06T19:22:36ZengSciendoMeasurement Science Review1335-88712013-06-0113313214110.2478/msr-2013-0022CGCI-SIFT: A More Efficient and Compact Representation of Local DescriptorSu Dongliang0Wu Jian1Cui Zhiming2Sheng Victor S.3Gong Shengrong4The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaDepartment of Computer Science, University of Central Arkansas, Conway 72035, USAThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThis paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local interest region around the interest point into two main sub-regions: an inner region and a peripheral region. Then we describe the divided regions with gradient histogram information for the inner region and contrast intensity information for the peripheral region respectively. The contrast intensity information is defined as intensity difference between an interest point and other pixels in the local region. Our experimental results demonstrate that the proposed descriptor performs better than SIFT and its variants PCA-SIFT and SURF with various optical and geometric transformations. It also has better matching efficiency than SIFT and its variants PCA-SIFT and SURF, and has the potential to be used in a variety of realtime applications.https://doi.org/10.2478/msr-2013-0022image matchingdescriptorsiftcgci-siftreal-time |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Su Dongliang Wu Jian Cui Zhiming Sheng Victor S. Gong Shengrong |
spellingShingle |
Su Dongliang Wu Jian Cui Zhiming Sheng Victor S. Gong Shengrong CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor Measurement Science Review image matching descriptor sift cgci-sift real-time |
author_facet |
Su Dongliang Wu Jian Cui Zhiming Sheng Victor S. Gong Shengrong |
author_sort |
Su Dongliang |
title |
CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor |
title_short |
CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor |
title_full |
CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor |
title_fullStr |
CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor |
title_full_unstemmed |
CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor |
title_sort |
cgci-sift: a more efficient and compact representation of local descriptor |
publisher |
Sciendo |
series |
Measurement Science Review |
issn |
1335-8871 |
publishDate |
2013-06-01 |
description |
This paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local interest region around the interest point into two main sub-regions: an inner region and a peripheral region. Then we describe the divided regions with gradient histogram information for the inner region and contrast intensity information for the peripheral region respectively. The contrast intensity information is defined as intensity difference between an interest point and other pixels in the local region. Our experimental results demonstrate that the proposed descriptor performs better than SIFT and its variants PCA-SIFT and SURF with various optical and geometric transformations. It also has better matching efficiency than SIFT and its variants PCA-SIFT and SURF, and has the potential to be used in a variety of realtime applications. |
topic |
image matching descriptor sift cgci-sift real-time |
url |
https://doi.org/10.2478/msr-2013-0022 |
work_keys_str_mv |
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