This paper describes a simple framework for automatically annotating images using non-parametric models of distributions of image features. We show that under this framework quite ...
Alexei Yavlinsky, Edward Schofield, Stefan M. R&uu...
This paper describes an ongoing project which seeks to contribute to a wider understanding of the realities of bridging the semantic gap in visual image retrieval. A comprehensive ...
Peter G. B. Enser, Christine J. Sandom, Paul H. Le...
In this paper, we describe three different methods for the classification and annotation of medical radiographs. The methods were applied in the medical image annotation tasks of ...
In image retrieval, most systems lack user-centred evaluation since they are assessed by some chosen ground truth dataset. The results reported through precision and recall assess...
We propose a probabilistic graphical model to represent weakly annotated images1 . This model is used to classify images and automatically extend existing annotations to new image...