3D Sub-query Expansion for Improving Sketch-Based Multi-view Image Retrieval
Yen-Liang Lin, Cheng-Yu Huang, Hao-Jeng Wang, Winston Hsu; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 3495-3502
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.
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bibtex]
@InProceedings{Lin_2013_ICCV,
author = {Lin, Yen-Liang and Huang, Cheng-Yu and Wang, Hao-Jeng and Hsu, Winston},
title = {3D Sub-query Expansion for Improving Sketch-Based Multi-view Image Retrieval},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}