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Experiments with multi-view multi-instance learning for supervised image classification

In this paper we empirically investigate the benefits of multi-view multi-instance (MVMI) learning for supervised image classification. In multi-instance learning, examples for learning contain bags of feature vectors and thus data from different views cannot simply be concatenated as in the singleinstance case. Hence, multi-view learning, where one classifier is built per view, is particularly attractive when applying multiinstance learning to image classification. We take several diverse image data sets—ranging from person detection to astronomical object classification to species recognition—and derive a set of multiple instance views from each of them. We then show via an extensive set of 10×10 stratified cross-validation experiments that MVMI, based on averaging predicted confidence scores, generally exceeds the performance of traditional single-view multi-instance learning, when using support vector machines and boosting as the underlying learning algorithms.