Attributes have recently shown to give excellent results
for category recognition. In this paper, we demonstrate
their performance in the context of image retrieval. First,
we show that retrieval based on attribute vectors gives re-
sults comparable to the state of the art if retrieving images
of a particular object. Second, we demonstrate that com-
bining attribute and Fisher vectors improves performance
for retrieval of the same object as well as categories. Third,
we implement an efficient coding technique for compressing
the combined descriptor to very small codes. Experimental
results on the Holidays dataset show that our approach
significantly outperforms the state-of-the-art, even for a
very compact representation of 16 bytes per image. Re-
trieving category images is evaluated on a web dataset set
with text annotation for learning representations. We present
a baseline for retrieval based on images only and show the
contribution of attribute features. Furthermore, we show
how the combined image features can supplement text fea-
tures.