Cited By
View all- Ahamed Shohag MZhao XJonathan Wu QPourpanah F(2022)Cross-graph reference structure based pruning and edge context information for graph matchingInformation Sciences10.1016/j.ins.2022.10.065Online publication date: Oct-2022
Graph Based Image Matching Using the Fusion of Several Kinds of Features
Authors: 
Md Shakil Ahamed Shohag, Muhammad Umair Hassan, Dongmei Niu, Xiangyu Kong, Xiuyang Zhao, Farzana RahmanAuthors Info & Claims
Pages 188 - 193
Abstract
We exploit the image saliency, grayscale and RGB information to find feature correspondence based on a graph matching technique. We consider these saliency features, grayscale and average RGB values along with distance information for extracted image features by using a detector-descriptor combination of MSER detector and SIFT Descriptor. Three different affinity matrices are introduced by utilizing single pixel values collected by using the feature vector coordinates from three different forms of the same images. Considerable improvements in image matching performance are obtained while these affinity matrices are combined separately with the distance-based affinity matrix.
References
[1]
Le Moigne, J., Campbell, W. J., & Cromp, R. F. (2002). An automated parallel image registration technique based on the correlation of wavelet features. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1849--1864.
[2]
Inglada, J., Muron, V., Pichard, D., & Feuvrier, T. (2007). Analysis of artifacts in subpixel remote sensing image registration. IEEE Transactions on Geoscience and Remote Sensing, 45(1), 254--264.
[3]
Liang, J., Liu, X., Huang, K., Li, X., Wang, D., & Wang, X. (2014). Automatic registration of multisensor images using an integrated spatial and mutual information (SMI) metric. IEEE transactions on geoscience and remote sensing, 52(1), 603--615.
[4]
Pang, S., Xue, J., Tian, Q., & Zheng, N. (2014). Exploiting local linear geometric structure for identifying correct matches. Computer Vision and Image Understanding, 128, 51--64.
[5]
Ma, J., Ma, Y., Zhao, J., & Tian, J. (2015). Image feature matching via progressive vector field consensus. IEEE Signal Processing Letters, 22(6), 767--771.
[6]
Liu, H., & Yan, S. (2010, June). Common visual pattern discovery via spatially coherent correspondences. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1609--1616). IEEE.
[7]
Cho, M., Lee, J., & Lee, K. M. (2010, September). Reweighted random walks for graph matching. In European conference on Computer vision (pp. 492--505). Springer, Berlin, Heidelberg.
[8]
Wu, J., Shen, H., Li, Y. D., Xiao, Z. B., Lu, M. Y., & Wang, C. L. (2013). Learning a hybrid similarity measure for image retrieval. Pattern Recognition, 46(11), 2927--2939.
[9]
Duchenne, O., Joulin, A., & Ponce, J. (2011, November). A graph-matching kernel for object categorization. In 2011 International Conference on Computer Vision (pp. 1792--1799). IEEE.
[10]
Berg, A. C., Berg, T. L., & Malik, J. (2005). Shape matching and object recognition using low distortion correspondences (pp. 26--33). IEEE.
[11]
Chen, H. T., Lin, H. H., & Liu, T. L. (2001). Multi-object tracking using dynamical graph matching. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 2, pp. II--II). IEEE.
[12]
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004). Robust wide-baseline stereo from maximally stable extremal regions. Image and vision computing, 22(10), 761--767.
[13]
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91--110.
[14]
Mikolajczyk, K., & Schmid, C. (2004). Scale & affine invariant interest point detectors. International journal of computer vision, 60(1), 63--86.
[15]
Lowe, D. G. (1999, September). Object recognition from local scale-invariant features. In iccv (Vol. 99, No. 2, pp. 1150--1157).
[16]
Palmer, S. E. (1999). Vision science: Photons to phenomenology. MIT press.
[17]
Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive psychology, 12(1), 97--136.
[18]
Achanta, R., Hemami, S., Estrada, F., & Süsstrunk, S. (2009). Frequency-tuned salient region detection. In IEEE international conference on computer vision and pattern recognition (CVPR 2009) (No. CONF, pp. 1597--1604).
[19]
Borji, A., & Itti, L. (2012, June). Exploiting local and global patch rarities for saliency detection. In 2012 IEEE conference on computer vision and pattern recognition (pp. 478--485). IEEE.
[20]
Cheng, M. M., Mitra, N. J., Huang, X., Torr, P. H., & Hu, S. M. (2015). Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 569--582.
[21]
Gao, D., Mahadevan, V., & Vasconcelos, N. (2008). The discriminant center-surround hypothesis for bottom-up saliency. In Advances in neural information processing systems (pp. 497--504).
[22]
Goferman, S., Zelnik-Manor, L., & Tal, A. (2012). Context-aware saliency detection. IEEE transactions on pattern analysis and machine intelligence, 34(10), 1915--1926.
[23]
Klein, D. A., & Frintrop, S. (2011, November). Center-surround divergence of feature statistics for salient object detection. In 2011 International Conference on Computer Vision (pp. 2214--2219). IEEE.
[24]
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., & Shum, H. Y. (2011). Learning to detect a salient object. IEEE Transactions on Pattern analysis and machine intelligence, 33(2), 353--367.
[25]
Lu, Y., Zhang, W., Lu, H., & Xue, X. (2011, November). Salient object detection using concavity context. In 2011 International Conference on Computer Vision (pp. 233--240). IEEE.
[26]
Perazzi, F., Krähenbühl, P., Pritch, Y., & Hornung, A. (2012, June). Saliency filters: Contrast based filtering for salient region detection. In 2012 IEEE conference on computer vision and pattern recognition (pp. 733--740). IEEE.
[27]
Navalpakkam, V., & Itti, L. (2006). An integrated model of top-down and bottom-up attention for optimizing detection speed. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (Vol. 2, pp. 2049--2056). ieee.
[28]
Liu, Z., Shi, R., Shen, L., Xue, Y., Ngan, K. N., & Zhang, Z. (2012). Unsupervised salient object segmentation based on kernel density estimation and two-phase graph cut. IEEE Transactions on Multimedia, 14(4), 1275--1289.
[29]
Huang, S., Wang, W., & Zhang, H. (2014, October). Retrieving images using saliency detection and graph matching. In 2014 IEEE International Conference on Image Processing (ICIP)(pp. 3087--3091). IEEE.
[30]
Cho, M., Lee, J., & Lee, K. M. (2009, September). Feature correspondence and deformable object matching via agglomerative correspondence clustering. In 2009 IEEE 12th International Conference on Computer Vision (pp. 1280--1287). IEEE.
[31]
Qin, Y., Feng, M., Lu, H., & Cottrell, G. W. (2018). Hierarchical cellular automata for visual saliency. International Journal of Computer Vision, 1--20.
[32]
Leordeanu, M., & Hebert, M. (2005, October). A spectral technique for correspondence problems using pairwise constraints. In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 (Vol. 2, pp. 1482--1489). IEEE.
[33]
Jiang, B., Tang, J., Ding, C., & Luo, B. (2015, March). A local sparse model for matching problem. In Twenty-Ninth AAAI Conference on Artificial Intelligence.
[34]
Cour, T., Srinivasan, P., & Shi, J. (2007). Balanced graph matching. In Advances in Neural Information Processing Systems (pp. 313--320).
[35]
Albarelli, A., Bulo, S. R., Torsello, A., & Pelillo, M. (2009, September). Matching as a non-cooperative game. In 2009 IEEE 12th International Conference on Computer Vision (pp. 1319--1326). IEEE.
[36]
Jiang, Bo, Jin Tang, Xiaochun Cao, and Bin Luo. "Lagrangian relaxation graph matching." Pattern Recognition 61 (2017): 255--265.
Index Terms
- Graph Based Image Matching Using the Fusion of Several Kinds of Features
Recommendations
Feature histogram equalization for feature contrast enhancement
Discover an inherited characteristic of feature descriptors named feature contrast.Relate feature contrast to the discrimination among the detected feature points.One feature histogram is formed along one dimension of all feature descriptors.Derive an ...
An improved feature extraction-based colour image matching method using quaternion matrix
Specific to the problem of feature description for colour image, a novel speeded-up robust features SURF descriptor of colour image-based quaternion CSURF-Q is proposed in this paper. Firstly, in order to represent the colour information of colour image ...
Image feature point matching method based on improved BRISK
In order to improve the accuracy of the BRISK algorithm of feature point matching, an improved BRISK feature point matching method is proposed by combining the SIFT algorithm idea with high matching accuracy. Firstly, the Gaussian image pyramid is ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2019
213 pages
ISBN:9781450371711
DOI:10.1145/3330393
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Shenzhen University: Shenzhen University
- Sun Yat-Sen University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 May 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Project of Jinan Research Leader Laboratory
- National Natural Science Foundation of China
- Doctoral Program of University of Jinan
Conference
ICMSSP 2019
ICMSSP 2019: 2019 4th International Conference on Multimedia Systems and Signal Processing
May 10 - 12, 2019
Guangzhou, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 74Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
Cited By
View all- Ahamed Shohag MZhao XJonathan Wu QPourpanah F(2022)Cross-graph reference structure based pruning and edge context information for graph matchingInformation Sciences10.1016/j.ins.2022.10.065Online publication date: Oct-2022
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in