Abstract
This chapter describes a new K-views algorithm, the K-views rotation-invariant features (K-views-R) algorithm, for texture image classification using rotation-invariant features. These features are statistically derived from a set of characteristic views for each texture. Unlike the basic K-views model such as K-views-T method, all the views used are transformed into rotation-invariant features, and the characteristic views (i.e., K-views) are selected randomly. This is in contrast to the basic K-views model that uses the K-means algorithm for choosing a set of characteristic views (i.e., K-views). In this new algorithm, the decision of assigning a pixel to a texture class is made by considering all those views, which have the pixel (being classified) located inside the boundary of their views. To preserve the primitive information of a texture class as much as possible, the new algorithm randomly selects K-views of the view set from each sample sub-image as the set of characteristic views.
Now the general who wins a battle makes many calculations in his temple ere the battle is fought. The general who loses a battle makes but few calculations beforehand. Thus do many calculations lead to victory and few calculations to defeat: how much more no calculation at all! It is by attention to this point that I can foresee who is likely to win or lose.
—Sun Tzu
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Hung, CC., Song, E., Lan, Y. (2019). Features-Based K-views Model. In: Image Texture Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-13773-1_7
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DOI: https://doi.org/10.1007/978-3-030-13773-1_7
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