Group feature selection in image classification with multiple kernel learning
Classification of large amount of images calls for diverse types of features, but employing all possible feature types will create unnecessary computation burden, and may result in reduced classification accuracy. Selecting feature vectors individually is not a feasible solution in this scenario due to the high amount of feature vectors needed for reasonable performance. Instead, this paper proposes a measure that effectively evaluates the relative significance of a feature group, employing the minimum redundancy maximum relevance (mRMR) feature selection. Multiple kernel learning (MKL) is used for combining different feature types in classification, which implicitly also serves an alternative way for weighing the feature groups' importance. Results show the proposed group feature selection better reflects a feature type's importance, and improve upon MKL performance. This study also finds that the convolutional neural network (CNN) features have the best discriminative power among all features, but it is still possible to improve classification accuracy with other well-designed features.