Presentation | 2013-11-13 Gaussian Sparse Hashing Koichiro SUZUKI, Mitsuru AMBAI, Ikuro SATO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Binary hashing has been widely used for Approximate Nearest Neighbor (ANN) search due to fast query speed and less storage cost. It is important for hashing functions to preserve a similarity of data space. We introduce a novel concept for preserving the similarity, a robustness index and an error cost function, and propose a hashing function learning method by optimizing the cost function with an assumption the data has Gaussian probability density function. Furthermore, we expand this method to learn a sparse hash function for faster coding. It is demonstrated that proposed methods have comparable level of similarity preservation to the existing methods. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | binary codes / approximate image retrieval / approximate nearest neighbor searching / sparse matrix |
Paper # | IBISML2013-56 |
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Committee | IBISML |
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Conference Date | 2013/11/5(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Gaussian Sparse Hashing |
Sub Title (in English) | |
Keyword(1) | binary codes |
Keyword(2) | approximate image retrieval |
Keyword(3) | approximate nearest neighbor searching |
Keyword(4) | sparse matrix |
1st Author's Name | Koichiro SUZUKI |
1st Author's Affiliation | DENSO ITLABORATORY, INC.() |
2nd Author's Name | Mitsuru AMBAI |
2nd Author's Affiliation | DENSO ITLABORATORY, INC. |
3rd Author's Name | Ikuro SATO |
3rd Author's Affiliation | DENSO ITLABORATORY, INC. |
Date | 2013-11-13 |
Paper # | IBISML2013-56 |
Volume (vol) | vol.113 |
Number (no) | 286 |
Page | pp.pp.- |
#Pages | 6 |
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