Abstract：To improve the quality of image edge feature extraction,quantum kernel clustering algorithm is proposed. Firstly, pixel mapping quantum was coded and pixel blocks in encode element domain were sampled randomly. Secondly, the distance between data point and every clustering core was calculated by clustering distance, data vector was distributed to the core vector at the minimum distance, and valid effect range was determined by kernel function. Finally, pixel cluster dissimilarity was analyzed and procedure was described. Experimental result shows that quantum kernel clustering algorithm can achieve the image edge feature extraction with well-defined profile and high consistency, good assessment factor MS and clustering precision, and fast convergence.
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2016, Vol. 37 
