Automatic image annotation using adaptive weighted distance in improved K nearest neighbors framework

Automatic image annotation is a challenging problem due to the label-image-matching, label-imbalance and label-missing problems. Some research tried to address part of these problems but didn__ integrate them. In this paper, an adaptive weighted distance method which incorporates the CNN (convolutional neural network) feature and multiple handcrafted features is proposed to handle the label-image-matching and label-imbalance issues, while the K nearest neighbors framework is improved by using the neighborhood with all labels which can reduce the effects of the label-missing problem. Finally, experiments on three benchmark datasets (Corel-5k, ESP-Game and IAPRTC-12) for image annotation are performed, and the results show that our approach is competitive to the state-of-the-art methods. © Springer International Publishing AG 2016.