Learning Hash Functions for Cross-View Similarity Search
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1360-1364.
Abstract
Many applications in Multilingual and Multimodal Information Access involve searching large databases of high dimensional data objects with multiple (conditionally independent) views. In this work we consider the problem of learning hash functions for similarity search across the views for such applications. We propose a principled method for learning a hash function for each view given a set of multiview training data objects. The hash functions map similar objects to similar codes across the views thus enabling cross-view similarity search. We present results from an extensive empirical study of the proposed approach which demonstrate its effectiveness on Japanese language People Search and Multilingual People Search problems.
@inproceedings{ijcai2011p230,
title = {Learning Hash Functions for Cross-View Similarity Search},
author = {Kumar, Shaishav and Udupa, Raghavendra},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1360--1364},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-230},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-230},
}
TY - CPAPER TI - Learning Hash Functions for Cross-View Similarity Search AU - Kumar, Shaishav AU - Udupa, Raghavendra T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1360 EP - 1364 DO - 10.5591/978-1-57735-516-8/IJCAI11-230 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-230 ER -Copied
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