Abstract

Multi-Instance learning (MIL) is a learning framework proposed recently and has been successfully used in scene and video classification and recognition. A novel Multi-Instance (MI) bag generating method is proposed in this paper, based on a Gaussian Mixed Model (GMM). The generated GMM is treated as an MI bag, of which the color and locally stable invariant components (SIFT) are the instances. Then, Agglomerative Information Bottleneck clustering is employed to transform the MIL problem into single-instance learning problem so that single-instance classifiers can be used for classification. Finally, ensemble learning is involved to further enhance classifiers' generalization ability. Experimental results demonstrate that the performance of the proposed framework for image recognition is superior to some common MI algorithms on average in a 5-category scene recognition task.

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