Abstract Automatically assigning keywords to images is of great interest as it allows one to retrieve, index, organize and understand large collections of image data. Many techniques have been proposed for image annotation in the last decade that give reasonable performance on standard datasets. However, most of these works fail to compare their methods with simple baseline techniques to justify the need for complex models and subsequent training. In this work, we introduce a new and simple baseline technique for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes global low-level image features and a simple combination of basic distance measures to find nearest neighbors of a given image. The keywords are then assigned using a greedy label transfer mechanism. The proposed baseline method outperforms the current state-of-theart methods on two standard and one large Web dataset. We believe that such a baseline measure will provide a strong platform to compare and better understand future annotation techniques.
For example, Makadia et al. REF utilized global low-level image features and a simple combination of basic distance measures to find nearest neighbors of a given image.
Baselines for Image Annotation
International Journal of Computer Vision
International Journal of Computer Vision
Computer Science