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Improved clustering criterion for image clustering with artificial bee colony algorithm

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Abstract

In this paper, a new objective function is proposed for image clustering and is applied with the artificial bee colony (ABC) algorithm, the particle swarm optimization algorithm and the genetic algorithm. The performance of the proposed objective function is tested on seven benchmark images by comparing it with the three well-known objective functions in the literature and the K-means algorithm in terms of separateness and compactness which are the main criterions of the clustering problem. Moreover, the Davies–Bouldin Index and the XB Index are also employed to compare the quality of the proposed objective function with the other objective functions. The simulated results show that the ABC-based image clustering method with the improved objective function obtains well-distinguished clusters.

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Authors and Affiliations

  1. Engineering Faculty, Computer Engineering Department, Erciyes University, Kayseri, Turkey

    Celal Ozturk, Emrah Hancer & Dervis Karaboga

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  1. Celal Ozturk
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  2. Emrah Hancer
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  3. Dervis Karaboga
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Correspondence to Emrah Hancer.

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Ozturk, C., Hancer, E. & Karaboga, D. Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Anal Applic 18, 587–599 (2015). https://doi.org/10.1007/s10044-014-0365-y

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