Semi-Supervised Learning Based Social Image Semantic Mining Algorithm

Abstract

As social image semantic mining is of great importance in social image retrieval, and it can also solve the problem of semantic gap. In this paper, a novel social image semantic mining algorithm based on semi-supervised learning is proposed. Firstly, labels which tagged the images in the test image dataset are extracted, and noisy semantic information are pruned. Secondly, the labels are propagated to construct an extended collection. Thirdly, image visual features are extracted from the unlabeled images by three steps, including watershed segmentation, region feature extraction and codebooks construction. Fourthly, vectors of image visual feature are obtained by dimension reduction. Fifthly, after the process of semi-supervised learning and classifier training, the confidence score of semantic terms for the unlabeled image are calculated by integrating different types of social image features, and then the heterogeneous feature spaces are divided into several disjoint groups. Finally, experiments are conducted to make performance evaluation. Compared with other existing methods, it can be seen than the proposed can effectively extract semantic information of social images