Adaptive Hybrid Feature Extraction for Leaf Image Classification by Support Vector Machine

In leaf image classification or retrieval fields, hybrid features are widely used to represent the information in various aspects by combining a number of sub-features linearly. However, the importance degrees of sub-features are often ignored by assigning the weights in an ad-hoc fashion without a solid theoretical basis. In this paper, a new type of adaptive hybrid features is proposed by using kernel trick of support vector machine (SVM), in which the weights can be adaptively selected. All weights are obtained by solving an optimization problem to maximize the discriminability of features. Experimental results of leaf image classification show that SVMs with new features significantly outperform those with traditional ones in terms of test accuracy.