  Automatic annotation of images with descriptive words is a challenging
problem with vast applications in the areas of image search and retrieval. This
problem can be viewed as a label-assignment problem by a classifier dealing
with a very large set of labels, i.e., the vocabulary set. We propose a novel
annotation method that employs two layers of sparse coding and performs
coarse-to-fine labeling. Themes extracted from the training data are treated as
coarse labels. Each theme is a set of training images that share a common
subject in their visual and textual contents. Our system extracts coarse labels
for training and test images without requiring any prior knowledge. Vocabulary
words are the fine labels to be associated with images. Most of the annotation
methods achieve low recall due to the large number of available fine labels,
i.e., vocabulary words. These systems also tend to achieve high precision for
highly frequent words only while relatively rare words are more important for
search and retrieval purposes. Our system not only outperforms various
previously proposed annotation systems, but also achieves symmetric response in
terms of precision and recall. Our system scores and maintains high precision
for words with a wide range of frequencies. Such behavior is achieved by
intelligently reducing the number of available fine labels or words for each
image based on coarse labels assigned to it.
