Boosting-based multiple kernel learning for image re-ranking
Proceedings of the international conference on Multimedia
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Scene images typically include diverse and distinctive properties. It is reasonable to consider different features in establishing a scene category recognition system with a promising performance. We propose an adaptive model to represent various features in a unified domain, i.e., a set of kernels, and transform the discriminant information contained in each kernel into a set of weak learners, called dyadic hyper cuts. Based on this model, we present a novel approach to carrying out incremental multiple kernel learning for feature fusion by applying AdaBoost to the union of the sets of weak learners. We further evaluate the performance of this approach by a benchmark dataset for scene category recognition. Experimental results show a significantly improved performance in both accuracy and efficiency.