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JACIII Vol.20 No.7 pp. 1035-1043
doi: 10.20965/jaciii.2016.p1035
(2016)

Paper:

Views over last 60 days: 1,043

Image Clustering Using Active-Constraint Semi-Supervised Affinity Propagation

Qi Lei*,**, Jun Liu*, Min Wu***,†, and Jie Wang*

*School of Information Science and Engineering, Central South University
Changsha 410083, China

**School of Engineering, University of South Wales
Pontypridd, CF37 1DL, United Kingdom

***School of Automation, China University of Geosciences
Wuhan 430074, China

†Corresponding author

Received:
July 5, 2016
Accepted:
August 14, 2016
Published:
December 20, 2016
Creative Commons License
Keywords:
image clustering, affinity propagation, active learning, image feature extraction
Abstract
Image clustering is an effective way to discover and analyze large quantities of image data. The HSV color space is particularly advantageous in image feature extraction because of its relatively prominent feature vector. The objective of this study is to develop an image clustering method using the active-constraint semi-supervised affinity propagation (ACSSAP) algorithm. The algorithm adds supervision to the affinity propagation (AP) clustering algorithm with pairwise constraints and uses active learning to guide the AP clustering algorithm. Active learning of pairwise constraints leads to an adjustment of the similarity matrix in AP at each iteration. In the experiments, the advantage of HSV space is analyzed and the ACSSAP algorithm is evaluated for data sets of different sizes in comparison with other algorithms. The result demonstrates that the ACSSAP has better performance.
Cite this article as:
Q. Lei, J. Liu, M. Wu, and J. Wang, “Image Clustering Using Active-Constraint Semi-Supervised Affinity Propagation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1035-1043, 2016.
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References
  1. [1] G. H. Liu and J. Y. Yang, “Contend-based Image retrieval Using Color Different Histogram,” Pattern Recognition, Vol.46, No.1, pp. 188-198, 2013.
  2. [2] C. Mohammad Ali Zare and C. Nasrollah, “Bridging the Semantic Gap for Automatic Image Annotation by Learning the Manifold Space,” Computer System Science and Engineering, Vol.30, No.4, pp. 303-316, 2015.
  3. [3] S. Gordon, H. Greenspan, and J. Goldberger, “Applying the Information Bottleneck Principle to Unsupervised Clustering of Discrete and Continuous Image Representations,” Proc. of the 9th IEEE Int. Conf. on Computer Vision, pp. 370-377, 2003.
  4. [4] S. L. Wu, H. D. Chen, and Z. Z. Zhao, “An Improved Remote Sensing Image Classification Based on K-Means Using HSV Color Feature,” Int. Conf. on Computational Intelligence and Security, pp. 201-204, 2014.
  5. [5] W. A. Albukhanajer, J. A. Briffa, and Y. C. Jin, “Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise,” IEEE Trans. on Cybernetics, Vol.45, No.9, pp. 1757-1768, 2015.
  6. [6] W. Snyder, Y. S. Han, G. Bilbro, R. Whitaker, and S. Pizer, “Image Relaxation: Restoration and Feature Extraction,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.17, No.6, pp. 620-624, 1995.
  7. [7] I. Dopido, A. Villa, S. Plaza, and P. Gamba, “A Quantitative and Comparative Assessment of Unmixing-based Feature Extraction Techniques for Hyperspectral Image Classification,” IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.5, No.2, pp. 421-435, 2012.
  8. [8] E. Izquierdo-Verdiguer, L. Gomez-Chova, L. Bruzzone, and G. Camps-Valls, “Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis,” IEEE Trans. on Geoscience and Remote Sensing, Vol.52, No.9, pp. 5567-5578, 2014.
  9. [9] A. R. Smith, “Color Gamut Transform Pairs,” ACM Siggraph Computer Graphics, Vol.12, No.3, pp. 12-19, 1978.
  10. [10] Y. X. Gui and F. X. Wang, “An Image Denoising Method for Noise Based on Mixed Gaussian Distribution in HSV Color Space,” J. of Information and Computational Science, Vol.12, No.4, pp. 1461-1467, 2015.
  11. [11] M. V. Latte, S. Shidnal, B. S. Anami, and V. B. Kuligod, “A Combined HSV and GLCM Approach for Paddy Variety Identification from Crop Images,” Int. J. of Signal Process, Image Processing and Pattern Recognition, Vol.8, No.10, pp. 221-232, 2015.
  12. [12] Q. Lei, M. Wu, and J. H. She, “Online Optimization of Fuzzy Controller for Coke-Oven Combustion Process Based on Dynamic Just-in-Time Learning,” IEEE Trans. on Automation Science and Engineering, Vol.112, No.4, pp. 1-6, 2013.
  13. [13] A. Malakar and J. Mukherjee, “Image Clustering Using Color Moments, Histogram, Edge and K-means Clustering,” Int. J. of Science and Research (IJSR), Vol.2, No.1, pp. 532-537, 2013.
  14. [14] B. J. Frey and D. Dueck, “Clustering by Passing Messages between Data Points,” Science, Vol.315, No.5814, pp. 972-976, 2007.
  15. [15] Y. Zhang and H. Zhang, “Image Clustering Based on SIFT-affinity Propagation,” 11th IEEE Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 358-362, 2014.
  16. [16] K. Wagstaff and C. Cardie, “Clustering with Instance-level Constraints,” 17th Int. Conf. on Machine Learning, pp. 1103-1110, 2000.
  17. [17] I. E. Givoni and B. J. Frey, “Semi-supervised Affinity Propagation with Instance-level Constraints,” Int. Conf. on Artificial Intelligence and Statistics, pp. 161-168, 2009.
  18. [18] H. Zeng and Y. M. Cheung, “Semi-supervised Maximum Margin Clustering with Pairwise Constraints,” IEEE Trans. on Knowledge and Data Engineering, Vol.24, No.5, pp. 926-939, 2012.
  19. [19] B. Settles, “Active Learning Literature Survey,” University of Wisconsin-Madison, Vol.52, No.55-66, pp.11, 2009.
  20. [20] M. Fang and X. Q. Zhu, “Active Learning with Uncertain Labeling Knowledge,” Pattern Recognition Letters, Vol.43, pp. 98-108, 2014.
  21. [21] N. Grira, M. Crucianu, and N. Boujemaa, “Active Semi-supervised Fuzzy Clustering,” Pattern Recognition, Vol.41, No.5, pp. 1834-1844, 2008.
  22. [22] Q. Lei, H. P. Yu, and M. Wu, “Active semi-supervised affinity propagation clustering algorithm based on pair-wise constraints,” the 11th IEEE World Congress on Intelligent Control and Automation, pp. 2304-2309, 2014.
  23. [23] A. Samat, J. Li, A. C. Liu, P. J. Du, Z. L. Miao, and J. Q. Luo, “Improved Hyperspectral Image Classification by Active Learning Using Pre-designed Mixed Pixels,” Pattern Recognition, Vol.51, pp. 43-58, 2016.
  24. [24] N. Alajlan, E. Pasolli, F. Melgani, and A. Franzoso, “Large-scale Image Classification Using Active Learning,” IEEE Geoscience and Remote Sensing Letters, Vol.11, No.1, pp. 259-263, 2014.
  25. [25] D. Klein, S. D. Kamvar, and C. D. Manning, “From Instance-level Constraints to Space-level Constraints: Making the Most of Prior Knowledge in Data Clustering,” 2002.
  26. [26] A. Strehl, J. Ghosh, and R. Mooney, “Impact of Similarity Measures on Web-page Clustering,” The Association for the Advancement of Artificial Intelligence (AAAI), pp. 58-64, 2000.
  27. [27] W. M. Rand, “Objective Criteria for the Evaluation of Clustering Methods,” J. of the American Statistical Association, Vol.66, No.336, pp. 846-850, 1971.

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