Abstract
This paper proposes a search-effective strategy for multi-class texture classification. The textures are classified according to the nearest neighbor rule based on our recently developed texture metric. We will show that it is possible to significantly reduce the amount of computation from an exhaustive search scheme. For this purpose, a distance-preserving vector space representation of the texture database is constructed. The representation facilitates the selection of a subset of class prototypes which constitute the reduced search space. In addition, the prototypes are organized into a hierarchy to further economize the search for the nearest class. This methodology is demonstrated by experiments on 720 texture samples belonging to eight classes. On average, a reduction of close to 70% is achieved.
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