a matrix modular svm robust to imbalanced data for efficient visual concept detection
In this paper, we propose a novel matrix modular support vector Machine (MMSVM) classifier that partitions an image retrieval task into many easier two-class tasks between subsets, each of which is accomplished by a SVM model, and then combines the outputs of the SVM models to produce the final decision. The classifier is tested on ImageClef2009 Photo Annotation, with a comparison with the single SVM model. The experimental results show that our MMSVM model performs well as a classifier in image retrieval, especially in enhancing the classification accuracy for positive samples. We also demonstrate that the MMSVM model has an apparent complementary classification capability to SVM. A good fusion on them might improve the accuracy of image retrieval.
svm imbalanced data image retrieval modular classification