A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

Yanyan Geng, Guohui Zhang, Weizhi Li, Yi Gu, Ru-Ze Liang, Gaoyuan Liang, Jingbin Wang, Yanbin Wu, Nitin Patil, Jing-Yan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations

Abstract

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.
Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2017
PublisherSpringer Nature
Pages539-546
Number of pages8
ISBN (Print)9783319686110
DOIs
StatePublished - Oct 25 2017

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