Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/83879
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Lin, G. | - |
dc.contributor.author | Shen, C. | - |
dc.contributor.author | Suter, D. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Proceedings, 2013 IEEE International Conference on Computer Vision, ICCV 2013: pp.2552-2559 | - |
dc.identifier.isbn | 9781479928392 | - |
dc.identifier.issn | 1550-5499 | - |
dc.identifier.uri | http://hdl.handle.net/2440/83879 | - |
dc.description.abstract | Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods. Our framework decomposes the hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art. | - |
dc.description.statementofresponsibility | Guosheng Lin, Chunhua Shen, David Suter, Anton van den Hengel | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.relation.ispartofseries | IEEE International Conference on Computer Vision | - |
dc.rights | © 2013 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/iccv.2013.317 | - |
dc.title | A general two-step approach to learning-based hashing | - |
dc.type | Conference paper | - |
dc.contributor.conference | IEEE International Conference on Computer Vision (14th : 2013 : Sydney, Australia) | - |
dc.identifier.doi | 10.1109/ICCV.2013.317 | - |
dc.publisher.place | USA | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Suter, D. [0000-0001-6306-3023] | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
Appears in Collections: | Aurora harvest Computer Science publications |
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RA_hdl_83879.pdf Restricted Access | Restricted Access | 321.49 kB | Adobe PDF | View/Open |
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