Multi-Instance Learning Algorithm Based on LSTM for Chinese Painting Image Classification
Daxiang Li, Yue Zhang
Abstract
Aiming at the problem of weakly supervised learning in traditional Chinese painting image classification, a novel multi-instance learning algorithm based on Long and Short-Term Memory neural network with attention mechanism (ALSTM-MIL) is proposed. Firstly, by using the Pyramid Overlapping Grid Division (POGP), a multi-instance modeling scheme is designed to convert Chinese painting images into multi-instance bag, thereby transforming the problem of Chinese painting image classification into a MIL problem. Secondly, an efficient sequence generator is designed. It selects discriminative instances from the positive bags, construct a discriminative instance set (DIS), and convert multi-instance bags into equal-length ordered sequences. Thirdly, an LSTM network model with an attention mechanism is designed to perform semantic analysis on multi-instance bags to obtain their memory coding features, and then combined with the Softmax classifier to achieve semantic classification of traditional Chinese painting images. Experimental results on the Chinese painting (CP) image set show that the LSTM network built on the visual feature set is feasible, and the performance of the proposed MIL algorithm is also superior to other classification algorithms.