Quaternion color texture segmentation

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• 作者：Lilong Shi ; Brian Funt
• 关键词：Quaternion color processing ; Quaternion principal components analysis ; Color texture analysis ; Image segmentation ; Color texture classification
• 刊名：Computer Vision and Image Understanding
• 出版年：2007
• 出版时间：July-August 2007
• 年：2007
• 卷：107
• 期：1-2
• 页码：88-96
• 全文大小：1866 K

文摘

The quaternion representation of color is shown here to be effective in the context of segmenting color images into regions of similar color texture. The advantage of using quaternion arithmetic is that a color can be represented and analyzed as a single entity. A low-dimensional basis for the color textures found in a given image is derived via quaternion principal component analysis (QPCA) of a training set of color texture samples. A color texture sample is then projected onto this basis to obtain a concise (single quaternion) description of the texture. To handle the large amount of training data, QPCA is extended to incremental QPCA. The power of the proposed quaternion color texture representation is demonstrated by its use in an unsupervised segmentation algorithm that successfully divides an image into regions on basis of texture.