Abstract
This comparative study employs several modified versions of the fuzzy c-means algorithm in image color reduction, with the aim of assessing their accuracy and efficiency. To assure equal chances for all algorithms, a common framework was established that preprocesses input images in terms of a preliminary color quantization, extraction of histogram and selection of frequently occurring colors of the image. Selected colors were fed to clustering by studied c-means algorithm variants. Besides the conventional fuzzy c-means (FCM) algorithm, the so-called generalized improved partition FCM algorithm, and several versions of the generalized suppressed FCM were considered. Accuracy was assessed by the average color difference between input and output images, while efficiency tests monitored the total runtime. All modified algorithms were found more accurate, and some suppressed models also faster than FCM.
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Szilágyi, L., Dénesi, G., Enăchescu, C. (2016). Fast Color Quantization via Fuzzy Clustering. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_12
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DOI: https://doi.org/10.1007/978-3-319-46681-1_12
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