  Cross-modal retrieval deals with retrieving relevant items from one modality,
when provided with a search query from another modality. Hashing techniques,
where the data is represented as binary bits have specifically gained
importance due to the ease of storage, fast computations and high accuracy. In
real world, the number of data categories is continuously increasing, which
requires algorithms capable of handling this dynamic scenario. In this work, we
propose a novel incremental cross-modal hashing algorithm termed "iCMH", which
can adapt itself to handle incoming data of new categories. The proposed
approach consists of two sequential stages, namely, learning the hash codes and
training the hash functions. At every stage, a small amount of old category
data termed "exemplars" is is used so as not to forget the old data while
trying to learn for the new incoming data, i.e. to avoid catastrophic
forgetting. In the first stage, the hash codes for the exemplars is used, and
simultaneously, hash codes for the new data is computed such that it maintains
the semantic relations with the existing data. For the second stage, we propose
both a non-deep and deep architectures to learn the hash functions effectively.
Extensive experiments across a variety of cross-modal datasets and comparisons
with state-of-the-art cross-modal algorithms shows the usefulness of our
approach.
