  Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient, since a common low-level subspace that traverses semantically
across the two modalities is non-trivial to establish. Most existing SBIR
studies do not directly tackle this cross-modal problem. This naturally
motivates us to explore the effectiveness of cross-modal retrieval methods in
SBIR, which have been applied in the image-text matching successfully. In this
paper, we introduce and compare a series of state-of-the-art cross-modal
subspace learning methods and benchmark them on two recently released
fine-grained SBIR datasets. Through thorough examination of the experimental
results, we have demonstrated that the subspace learning can effectively model
the sketch-photo domain-gap. In addition we draw a few key insights to drive
future research.
