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.
@article{arxiv.1705.09888,
title = {Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval},
author = {Peng Xu and Qiyue Yin and Yongye Huang and Yi-Zhe Song and Zhanyu Ma and Liang Wang and Tao Xiang and W. Bastiaan Kleijn and Jun Guo},
journal= {arXiv preprint arXiv:1705.09888},
year = {2017}
}