<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor

The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China

Department of Computer Science, University of Central Arkansas, Conway 72035, USA

Measurement Science Review

{"article-title":"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...

CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor

Published Online: Jun 21, 2013

Page range: 132 - 141

DOI: https://doi.org/10.2478/msr-2013-0022

This content is open access.