A traditional approach to retrieving images is to manually annotate the image with textual keywords and then retrieve images using these keywords. Manual annotation is expensive and recently a few approaches have been proposed for automatically annotating images. These techniques usually learn a statistical model using a training set of images annotated with keywords and use this model to automatically annotate test images. While promising, these techniques have generally been tested on a few thousand images, with vocabularies of a few hundred words or less and using relatively high quality training data where the keywords are categories/objects and are directly correlated with the visual data.

Here, we investigate the problem of automatically annotating a large dataset of news photographs using low quality training data and a large vocabulary. We use 56,117 images and captions from Yahoo News Photos for our training and test data. The captions in the training portion of this data often contain a great deal of text most of which does not directly describe the image and as labels are, therefore noisy. We use the Normalized Continuous Relevance Models for our annotation and discuss how to speed up the model (by a factor of 10) using a voting technique. An improved distance measure also improves precision. To handle noisy text data and the large vocabulary of 4073 words, we investigate using different kinds of words for training and show that words which describe the content of the picture are significantly more useful for annotating images. Previous work on annotating images has largely dealt with high quality keywords.