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| A Multi-instance Multi-label Image Classification Method Based on Sparse Coding and Ensemble Learning |
| Song Xiang-fa Jiao Li-cheng |
| Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China |
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Abstract This paper presents a novel multi-instance multi-label image classification method based on sparse coding and ensemble learning. First, a dictionary is learned based on all the instances in the training bags, and the sparse coding coefficient of each instance is calculated according to the dictionary; Second, a bag feature vector is computed based on all the sparse coding coefficients of the bag. Multi-instance multi-label issue is transformed into multi-label issue that can be solved by the multi-label algorithm. Ensemble learning is involved to enhance further the classifiers’ generalization. Experimental results on multi-instance multi-label image data show that the proposed method is superior to the state-of-art methods in terms of metrics.
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Received: 19 September 2012
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Corresponding Authors:
Song Xiang-fa
E-mail: xiangfasong@163.com
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2013, Vol. 35 
