关键词: 图像自动标注, 深度学习, 集成学习, 机器学习

Abstract: The performance of text-based image retrieval is highly dependent on manual tagging, and the deep learning can be used to realize image keywords generated automatically. Combining the predictions of many different large neural nets is an effective way for improving the classification accuracy. Firstly, for improving the generalization performance of the deep learning model, this paper proposes the Dropout algorithm. Dropout is a technique for addressing this problem by randomly dropping units (along with their connections) from the neural network during training. So the algorithm is equivalent to train many neural networks for prediction. Next, by the reason of the diverse keywords of image, this paper proposes a two steps algorithm for image annotation. First step, the keywords are divided into three parts: base keywords, candidate keywords and irrelevant keywords depending on the output of all neural networks. Second step, the keywords are chosen in candidate set depending on their correlation with base keywords. At last, the base keywords and chosen keywords are labeled for images. Conducting extensive experiments on three popular data sets, the results demonstrate that the proposed framework can achieve favorable performance for image annotation.

Key words:  image auto-annotation, deep learning, assemble learning, machine learning