Automatic Image Annotation Using Adaptive Weighted Distance in Improved K Nearest Neighbors Framework

Jiancheng Li, Chun Yuan

Abstract: 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’t 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.