Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification

To solve the unbalanced data problems of learning models for semantic concepts,an optimized modeling method based on the posterior probability support vector machine(PPSVM)is presented.A neighbor-based posterior probability estimator for visual concepts is provided.The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models,as well as in improved robustness with respect...