  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.
