Similar image search over a large image database has been
attracting a lot of attention recently. The widely-used solution is to use a set of codes, which we call bag-of-delegates,
to represent each image, and to build inverted indices to organize the image database. The search can be conducted
through the inverted indices, which is the same to the way
of using texts to index images for search and has been shown
to be efficient and effective.
In this paper, we propose a tiny bag-of-delegates representation that uses a small amount of delegates with a high
search performance guaranteed. The main advantage is that
less storage is required to save the inverted indices while
having a high search accuracy. We propose an adaptive forward selection scheme to sequentially learn more and more
inverted indices that are constructed using spatial partition
trees. Experimental results demonstrate that our approach
can require a smaller number of delegates while achieving
the same accuracy and taking similar time.