  For some images, descriptions written by multiple people are consistent with
each other. But for other images, descriptions across people vary considerably.
In other words, some images are specific $-$ they elicit consistent
descriptions from different people $-$ while other images are ambiguous.
Applications involving images and text can benefit from an understanding of
which images are specific and which ones are ambiguous. For instance, consider
text-based image retrieval. If a query description is moderately similar to the
caption (or reference description) of an ambiguous image, that query may be
considered a decent match to the image. But if the image is very specific, a
moderate similarity between the query and the reference description may not be
sufficient to retrieve the image.
  In this paper, we introduce the notion of image specificity. We present two
mechanisms to measure specificity given multiple descriptions of an image: an
automated measure and a measure that relies on human judgement. We analyze
image specificity with respect to image content and properties to better
understand what makes an image specific. We then train models to automatically
predict the specificity of an image from image features alone without requiring
textual descriptions of the image. Finally, we show that modeling image
specificity leads to improvements in a text-based image retrieval application.
