Multiclass boosting SVM using different texture features in HEp-2 cell staining pattern classification

In this paper, we present four image descriptors for HEp-2 cell staining patterns classification, including LBP, Gabor, DCT, and a global appearance statistical descriptor. A multiclass boosting SVM algorithm is proposed to integrate these descriptors together: (1) within each boosting round, four multiclass posterior probability SVMs are trained corresponding to four descriptors, and then combined to an integrated classifier; (2) AdaBoost.M1 is modified to enhance the performance of the integrated classifiers. Experimental results over 721 images with 5-fold cross validation show the proposed method is effective and can improve the classification accuracy.