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

Paper:

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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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