Color image classification and retrieval through ternary decision structure based multi-category TWSVM

In this paper, we propose Ternary Decision Structure based multi-category twin support vector machines (TDS-TWSVM) classifier. Twin support vector machines (TWSVM) formulation deals with finding non-parallel plane classifier which is obtained by solving two related Quadratic Programming Problems (QPPs). The proposed TDS-TWSVM classifier is an extension of TWSVM so as to handle multi-class data and is more efficient in terms of training and testing time of classifiers. For a K-class problem, a balanced ternary structure requires ⌈log3K⌉ comparisons for evaluating a test sample. The experimental results depict that TDS-TWSVM outperforms One-Against-All TWSVM (OAA-TWSVM) and binary tree-based TWSVM (TB-TWSVM) in terms of classification accuracy. We have shown the efficacy of the proposed algorithm via image classification and further for image retrieval. Experiments are performed on a varied range of benchmark image databases with 5-fold cross validation.