A Novel Image Tag Completion Method Based on Convolutional Neural Transformation
Type
Conference PaperAuthors
Geng, YanyanZhang, Guohui
Li, Weizhi
Gu, Yi
Liang, Ru-Ze

Liang, Gaoyuan
Wang, Jingbin
Wu, Yanbin
Patil, Nitin
Wang, Jing-Yan
KAUST Department
Material Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Date
2017-10-25Online Publication Date
2017-10-25Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/626775
Metadata
Show full item recordAbstract
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.Citation
Geng Y, Zhang G, Li W, Gu Y, Liang R-Z, et al. (2017) A Novel Image Tag Completion Method Based on Convolutional Neural Transformation. Lecture Notes in Computer Science: 539–546. Available: http://dx.doi.org/10.1007/978-3-319-68612-7_61.Publisher
Springer NatureConference/Event name
26th International Conference on Artificial Neural Networks, ICANN 2017Additional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-68612-7_61ae974a485f413a2113503eed53cd6c53
10.1007/978-3-319-68612-7_61