Automatic Image Annotation Combining Semantic Neighbors and Deep Features
KE Xiao, ZHOU Mingke, NIU Yuzhen
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University, Fuzhou 350116
Abstract：In the traditional image annotation methods, the manual selection of features is time-consuming and laborious. In the traditional label propagation algorithm, semantic neighbors are ignored. Consequently visual similarity and semantic dissimilarity are caused and annotation results are affected. To solve these problems, an automatic image annotation method combining semantic neighbors and deep features is proposed. Firstly, a unified and adaptive depth feature extraction framework is constructed based on deep convolutional neural network. Then, the training set is divided into semantic groups and the neighborhood image sets of the unannotated images are set up. Finally, according to the visual distance, the contribution value of each label of the neighborhood images is calculated and the keywords are obtained by sorting their contribution values. Experiments on benchmark datasets show that compared with the traditional synthetic features, the proposed deep feature possesses lower dimension and better effect. The problem of visual similarity and semantic dissimilarity in visual nearest neighbor annotation method is improved, and the algorithm effectively enhances the accuracy and the number of accurate predicted tags.
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2017, Vol. 30 
