تقرير
Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
| العنوان: | Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval |
|---|---|
| المؤلفون: | Xu, Peng, Yin, Qiyue, Huang, Yongye, Song, Yi-Zhe, Ma, Zhanyu, Wang, Liang, Xiang, Tao, Kleijn, W. Bastiaan, Guo, Jun |
| سنة النشر: | 2017 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted by Neurocomputing |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1705.09888 |
| رقم الأكسشن: | edsarx.1705.09888 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |