具抗旋轉及縮放之紋理影像分類器

An Effective Classification Algorithm for Texture Resisting Rotating and Scaling

陳昱廷(Yu-tin Chen)

指導教授 ： 姚志佳

朝陽科技大學/資訊學院/資訊工程系/碩士(2010年)

摘要

在這篇論文中我們提出了一個具有效的克服旋轉及縮放問題混
合型的分類器，這個分類器中包含了最佳特徵空間區域及賈伯濾波器
的多角度頻域演算法，在抗旋轉的部份我們利用Gobor Wavelets(GWs)
的多頻域解析特性來擷取圖形特徵，抗縮放方面則以特徵區域比對的
概念來進行空間相似性的匹配，在本論文中提出了一個針對縮放圖形
的演算法去找出最佳空間區塊當成我們的特徵區塊，並對其特徵空間
區塊計算出四個抗縮放的參數來比對每個空間區塊的相似度，經實驗
結果證明在抗旋轉及抗縮放辨識效果比其他方法來的好。

關鍵字

角度頻域演算法 ； 抗縮放 ； 賈伯濾波器 ； 最佳特徵空間 ； 抗旋轉 ； 多頻域解析

並列摘要

In this paper an effective classification algorithm based on
significant region and Gabor multi-frequency domain was proposed to overcome the identification problems of the texture image caused by the rotating and scaling. On rotation invariant, Gabor wavelets are used to acquire the texture’s features. On scaling resisting, the similarity between two textures was calculated by comparing the significant region. In our proposed algorithm the wavelet transform and clustering method were used to locate the significant region so that four scaling resistance
parameters were generated from the significant region. Then, support vector machines (SVMs) are introduced as the classifier. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.

並列關鍵字

multi-dimensional ； multi-frequency ； Gobor Wavelets ； rotation invariant ； scaling invariant ； optimal feature spatial

參考文獻

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