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  • 學位論文

具有抗平移、旋轉及縮放能力之彩色紋理檢索

Translation, rotation and scale invariant color texture retrieval

指導教授 : 陳淑媛
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摘要


在這篇論文我們將提出一個以特徵分佈為基礎的彩色紋理影像檢索。亦即我們利用顏色和區域性的邊緣樣式資訊來比對兩彩色紋理區塊的同質性。此一同質性比對法,可用於彩色紋理資料庫之檢索。值得一提的是,本論文將色彩和邊緣的紋理特徵作一致性處理,不同於以往之方法僅將灰階紋理分析方法延伸運用於彩色影像的每個頻道,或僅分析色彩之特性,所以所提方法能得較佳檢索效果。另外我們還利用前述之同質性測量比對法,進行影像區塊切割方法,以達到針對自然影像資料庫進行彩色紋理區塊檢索的目的。所提方法之另一特色是因為所提比對特徵皆有抗平移、旋轉及縮放之能力,故本法可用以檢索平移、旋轉及縮放等變形之紋理。由眾多的實驗數據也確實證明我們的方法是有其可行性。

並列摘要


A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply extend gray-level texture analysis to each channel of the color images, or analyze only color characteristics. More specifically, the distributions of color and local edge patterns are used to derive similarity measure of a pair of textured regions for color texture retrieval. Moreover, a segmentation method based on the similarity measure is employed to extend the retrieval method to natural image database. More important, since the proposed feature distributions are invariant to translation, rotation and scale variations, our method has the ability to retrieve textures which are changed in translation, rotation and scale. The effectiveness and practicability of the proposed method have been proven by various experiments.

參考文獻


[1]C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Region-based image querying,” CVPR’97 Workshop on Content-Based Access of Image and Video Libraries, 1997
[2]K.M. Chen and S.Y.Chen, “Color texture segmentation using feature distributions,” CVGIP, pp. 198-205, 2000.
[4]F.S. Cohen, Z. Fan and M.A. Patel, “Classification of rotated and scale textured images using Gaussian Markov random field models,” IEEE Transactions Pattern Analysis Machine Intelligence, vol. 13, pp. 192-202, 1991.
[5]L.S. Davis, “Polarograms: a new tool for texture analysis,” Pattern Recognition, vol. 13, pp. 219-223, 1981.
[6]L.S. Davis, S.A. Johns and J.K. Aggarwal, “Texture analysis using generalized co-occurrence matrices,” IEEE Transactions Pattern Analysis Machine Intelligence, vol. 1, pp. 251-259, 1979.

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