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Title: Effective image clustering based on human mental search
Authors: Mousavirad, Seyed Jalaleddin
Ebrahimpour-Komleh, Hossein
Schaefer, Gerald
Keywords: Image clustering
Metaheuristic algorithms
Human mental search
Image segmentation
Issue Date: 2019
Publisher: © Elsevier
Citation: MOUSAVIRAD, S.J., EBRAHIMPOUR-KOMLEH, H. and SCHAEFER, G., 2019. Effective image clustering based on human mental search. Applied Soft Computing, 78, pp.209-220.
Abstract: Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as k-means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima. In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions. In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validity index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques.
Description: This paper is closed access until 13 February 2020.
Sponsor: Authors are grateful to University of Kashan for supporting this work under grant No. 572086.
Version: Accepted for publication
DOI: 10.1016/j.asoc.2019.02.009
URI: https://dspace.lboro.ac.uk/2134/37402
Publisher Link: https://doi.org/10.1016/j.asoc.2019.02.009
ISSN: 1568-4946
Appears in Collections:Closed Access (Computer Science)

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