Quaternion color texture segmentation

Authors:
Lilong Shi;Brian Funt
Affiliations:
School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6;School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6
Venue:
Computer Vision and Image Understanding
Year:
2007

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Abstract

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.