Texture classification using combined feature sets

We consider two methods to combine texture descriptions for classification: a composite feature vector which combines data additively, and an extended k-nearest-neighbour (KNN) rule which returns a decision based on the highest confidence in features, both aimed to improve classification capability. These have been used to combine a wide range of relatively simple texture features, and have been shown to have significant advantage. Although nearly all previous approaches have used a limited subset of the Brodatz database, the new techniques have been applied to the whole Brodatz database with evaluation independent of the number of test classes used by measuring the number of perfect classes. The results of these new methods of combination show that an overall classification rate exceeding 90% can be achieved with 71 perfect classes, improving capabilities above using the measures individually.