Image Classification Algorithm Based on Bag-Level Space Multiple Instance Learning with Sparse Representation

The classification algorithm based on multiple instance learning(MIL) has a good performance due to the MIL has disambiguate ability. However, the bag-level space multiple instance learning algorithms always ignore the small target region and contains a large amount of redundant information during feature selection, which may cause the information loss for partial bags and can affect the performance of classification. In this paper, we proposed an improved multiple instance learning classification algorithm based on the framework of multiple instance learning and the sparse coding. Firstly, according to the characteristics of similar samples can cluster into one class, k-means algorithm is used to construct the visual vocabulary for each class of images. To eliminate redundant information, the negative characteristic of negative samples in negative bags is used to constrain the visual vocabulary. The bag feature vectors for each class of training samples are achieved by computing the similarity between the training sample and the visual vocabulary. Then, sparse coding is used to achieve the dictionary matrix for each class of the training samples. Finally, the labels for test images are predicted by linear combination of the dictionary and coefficient, which is achieved in training data, to represent the bag-level features for test images. Experimental results on COREL image data show that the proposed algorithm can better solve the problems in multiple instance learning image classification and achieve higher classification accuracy compared with the other multiple instance learning based image classification algorithms. © 2017, Editorial Board of Journal of Northwestern Polytechnical University. All right reserved.