Frustratingly Easy Cross-Modal Hashing
| Autor: | Ran He, Dekui Ma, Xiangwei Kong, Jian Liang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Theoretical computer science
Universal hashing Computer science business.industry Dynamic perfect hashing Hash function LabelMe 02 engineering and technology Machine learning computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Binary code Feature hashing Artificial intelligence business computer |
| Zdroj: | ACM Multimedia |
| Popis: | Cross-modal hashing has attracted considerable attention due to its low storage cost and fast retrieval speed. Recently, more and more sophisticated researches related to this topic are proposed. However, they seem to be inefficient computationally for several reasons. On one hand, learning coupled hash projections makes the iterative optimization problem challenging. On the other hand, individual collective binary codes for each content are also learned with a high computation complexity. In this paper we describe a simple yet effective cross-modal hashing approach that can be implemented in just three lines of code. This approach first obtains the binary codes for one modality via unimodal hashing methods (e.g., iterative quantization (ITQ)), then applies simple linear regression to project the other modalities into the obtained binary subspace. Obviously, it is non-iterative and parameter-free, which makes it more attractive for many real-world applications. We further compare our approach with other state-of-the-art methods on four benchmark datasets (i.e., the Wiki, VOC, LabelMe and NUS-WIDE datasets). Despite its extraordinary simplicity, our approach performs remarkably and generally well for these datasets under different experimental settings (i.e., large-scale, high-dimensional and multi-label datasets). |
| Databáze: | OpenAIRE |
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