3D Sub-Query Expansion for Improving Sketch-Based Multi-View Image Retrieval
Abstract
We propose a 3D sub-query expansion approach for boosting sketch-based multi-view image retrieval. The core idea of our method is to automatically convert two (guided) 2D sketches into an approximated 3D sketch model, and then generate multi-view sketches as expanded sub-queries to improve the retrieval performance. To learn the weights among synthesized views (sub-queries), we present a new multi-query feature to model the similarity between subqueries and dataset images, and formulate it into a convex optimization problem. Our approach shows superior performance compared with the state-of-the-art approach on a public multi-view image dataset. Moreover, we also conduct sensitivity tests to analyze the parameters of our approach based on the gathered user sketches.
Cite
Text
Lin et al. "3D Sub-Query Expansion for Improving Sketch-Based Multi-View Image Retrieval." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.434Markdown
[Lin et al. "3D Sub-Query Expansion for Improving Sketch-Based Multi-View Image Retrieval." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/lin2013iccv-3d/) doi:10.1109/ICCV.2013.434BibTeX
@inproceedings{lin2013iccv-3d,
title = {{3D Sub-Query Expansion for Improving Sketch-Based Multi-View Image Retrieval}},
author = {Lin, Yen-Liang and Huang, Cheng-Yu and Wang, Hao-Jeng and Hsu, Winston},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.434},
url = {https://mlanthology.org/iccv/2013/lin2013iccv-3d/}
}