Cross-modal subspace learning for fine-grained sketch-based image retrieval

@article{Xu2018CrossmodalSL,
  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},
  year={2018},
  volume={abs/1705.09888}
}
  • Peng Xu, Qiyue Yin, +6 authors Jun Guo
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • 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… CONTINUE READING

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