Fast Online Hashing with Multi-Label Projection
- URL: http://arxiv.org/abs/2212.03112v1
- Date: Sat, 3 Dec 2022 03:19:28 GMT
- Title: Fast Online Hashing with Multi-Label Projection
- Authors: Wenzhe Jia, Yuan Cao, Junwei Liu, Jie Gui
- Abstract summary: We propose a novel Fast Online Hashing (FOH) method which only updates the binary codes of a small part of the database.
The experimental results show that the proposed FOH can achieve dramatic superiority on query time up to 6.28 seconds less than state-of-the-art baselines.
- Score: 15.85793225585693
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hashing has been widely researched to solve the large-scale approximate
nearest neighbor search problem owing to its time and storage superiority. In
recent years, a number of online hashing methods have emerged, which can update
the hash functions to adapt to the new stream data and realize dynamic
retrieval. However, existing online hashing methods are required to update the
whole database with the latest hash functions when a query arrives, which leads
to low retrieval efficiency with the continuous increase of the stream data. On
the other hand, these methods ignore the supervision relationship among the
examples, especially in the multi-label case. In this paper, we propose a novel
Fast Online Hashing (FOH) method which only updates the binary codes of a small
part of the database. To be specific, we first build a query pool in which the
nearest neighbors of each central point are recorded. When a new query arrives,
only the binary codes of the corresponding potential neighbors are updated. In
addition, we create a similarity matrix which takes the multi-label supervision
information into account and bring in the multi-label projection loss to
further preserve the similarity among the multi-label data. The experimental
results on two common benchmarks show that the proposed FOH can achieve
dramatic superiority on query time up to 6.28 seconds less than
state-of-the-art baselines with competitive retrieval accuracy.
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