A Boosting Approach to Exploit Instance Correlations for Multi-Instance Classification
We propose a Boosting approach for multi-instance (MI) classification. Lp-norm is integrated to localize the witness instances and formulate the bag scores from classifier outputs. The contributions are twofold. First, a flexible and concise model for Boosting is proposed by the Lp-norm localization and exponential loss optimization. The scores for bag-level classification are directly fused from the instance feature space without probabilistic assumptions. Second, gradient and Newton descent optimizations are applied to derive the weak learners for Boosting. In particular, the instance correlations are exploited by fitting the weights and Newton updates for the weak learner construction. The final Boosted classifiers are the sums of iteratively chosen weak learners. Experiments demonstrate that the proposed Lp-norm-localized Boosting approach significantly improves the MI classification performance. Compared with the state of the art, the approach achieves the highest MI classification accuracy on 7/10 benchmark data sets.