Efficient Learning of Sample-Specific Discriminative Features for Scene Classification

Learning the sample-specific discriminative features based on numerous local learning may not scale well to real world scene classification tasks and suffer from the risk of overfitting. Hence we cast it in SVM based localized multiple kernel learning framework, and design a new strategy to alternately optimize the standard SVM solver and the sample-specific kernel weights, by either a linear programming (for l₁ -norm) or with closed-form solutions (for l_p-norm). Experiments on both natural scene dataset and cluttered indoor scene dataset demonstrate the effectiveness and efficiency of our approach.