Features-Based K-views Model
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
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Features-Based K-views Model
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
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.
7.1
Rotation-Invariant Features
Although the K-views datagram (K-views-D) algorithm performs better than the K-views template (K-views-T) algorithm, the classification accuracy in the boundary areas are still a challenging problem for the K-views model. In addition, the “characteristic views” extracted for the K-views-T algorithm are not rotation-invariant, which leads to the recognition of different texture classes cannot be correctly classified when the image is rotated. A new K-views algorithm is developed in order to extract the rotation-invariant features of texture images and © Springer Nature Switzerland AG 2019 C.-C. Hung et al., Image Texture Analysis, https://doi.org/10.1007/978-3-030-13773-1_7
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7 Features-Based K-views Model
use them for improving the classification [5]. In this new algorithm, the K-views are randomly selected from the view set of each texture class as the “characteristic views”, which is different from the existing K-views-T and K-views-D algorithms. Then, we extract the rotation-invariant features from the “characteristic views”. In the process of classification, the decision that a pixel belongs to which texture class is made by considering all the views which consist of the pixel being classified. The rotation-invariant features of each of these corresponding views in the image being classified are also calculated. To develop a new K-views algorithm, a set of rotation-invariant features will be extracted from all the characteristic views, which are obtained using the K-views-T algorithm as described in Chap 5. The rotation-invariant features are then used in the new K-views algorithm which will be
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