以Kernel為基礎之模糊分群演算法硬體架構實現

Kernel-Based Fuzzy C-Means with Spatial Constraint Clustering Algorithm Hardware Implementation

歐浩聲(Hao-Sheng Ou)

指導教授 ： 黃文吉

國立臺灣師範大學/理學院/資訊工程研究所/碩士(2012年)

摘要

本論文根據文獻[12]以及文獻[17]，以此兩則文獻中提到的FCM-SC分群演算法的硬體架構和KFCM演算法的硬體架構為基礎，實作以非線性高斯核函式為核距離計算之KFCM[12] 再加上空間資訊[17] 後的分群演算法硬體電路，具有管線化以及可以同時計算所有分群之權重係數的能力。此架構改良了以往KFCM分群演算法對於有雜訊的資料做分群的問題，並且配合KFCM本身可以對非線性資料分群效果較好的能力，所以能夠廣泛地使用在許多的分群資料上，並且都有良好的辨識率。本論文使用FPGA實現我們提出的硬體架構，並使用人工雜訊圖片作為實驗測試資料。實驗結果顯示本架構對於有雜訊的非線性資料分群效果確實較KFCM佳，且架構簡單提供了日後高度的延伸性。

關鍵字

可程式邏輯陣列 ； KFCM演算法 ； FCM-SC演算法 ； 系統程式晶片設計 ； KFCM-SC演算法

並列摘要

Based on the FCM-SC (Fuzzy C-Mean with spatial constraint) architecture in reference [12] and the KFCM (Kernel-Based Fuzzy C-Means) architecture in reference [17], KFCM-SC (Kernel-Based Fuzzy C-Means with spatial constraint) hardware architecture is proposed here with non-linear Gaussian kernel function and spatial constraint. Moreover, the KFCM-SC architecture also takes the advantage of the pipeline and it can compute all of the membership coefficients and centers concurrently. Compared to KFCM architecture, KFCM-SC architecture improves the segmentation ability for noisy data by computing the spatial information. With these advantages, it can deal with the non-linear data due to the kernel function, KFCM-SC architecture can be applied to wide of data and it can achieve better segmentation results. KFCM-SC architecture is implemented on FPGA and tested with noisy picture data. The segmentation result shows that KFCM-SC architecture definitely has a better ability with non-linear noisy data compared to KFCM. Because of the simple architecture of the KFCM-SC, it can be extended easily.

並列關鍵字

FPGA ； KFCM algorithm ； FCM-SC algorithm ； SOPC ； KFCM-SC algorithm.

參考文獻

[2] S. Chen, D. Zhang, Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Systems, Man, and Cybernetics, pp.1907-1919, 2004.

[3] W. Cai, S. Chen, D. Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition, Vol. 40, pp. 825-838, 2007.

[4] M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, A survey of kernel and spectral methods for clustering, Pattern Recognition, Vol. 41, pp. 176-190, 2008.

[5] K. S. Chuang, H. L. Tzeng, S. Chen, J. Wu, T. J. Chen, Fuzzy c-means clustering with spatial information for image segmentation, Comput. Med. Imaging Graphics, Vol. 30, pp.9-15, 2006.

[6] J. F. Kolen and T. Hutcheson, Reducing the Time Complexity of the Fuzzy C-Means Algorithm, IEEE Trans. Fuzzy Systems, Vol. 10, pp. 263-267, 2002.