A Multi-instance Multi-label Image Classification Method Based on Sparse Coding and Ensemble Learning

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.