  Hash codes are a very efficient data representation needed to be able to cope
with the ever growing amounts of data. We introduce a random forest semantic
hashing scheme with information-theoretic code aggregation, showing for the
first time how random forest, a technique that together with deep learning have
shown spectacular results in classification, can also be extended to
large-scale retrieval. Traditional random forest fails to enforce the
consistency of hashes generated from each tree for the same class data, i.e.,
to preserve the underlying similarity, and it also lacks a principled way for
code aggregation across trees. We start with a simple hashing scheme, where
independently trained random trees in a forest are acting as hashing functions.
We the propose a subspace model as the splitting function, and show that it
enforces the hash consistency in a tree for data from the same class. We also
introduce an information-theoretic approach for aggregating codes of individual
trees into a single hash code, producing a near-optimal unique hash for each
class. Experiments on large-scale public datasets are presented, showing that
the proposed approach significantly outperforms state-of-the-art hashing
methods for retrieval tasks.
