Presentation 2009-06-19
Memory Reduction with Bloomier Filters for Specific Object Recognition
Katsufumi INOUE, Koichi KISE,
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Abstract(in English) Specific object recognition based on nearest neighbor search of feature vectors required a huge amount of memory to store all feature vectors for distance calculation. To solve this problem, we propose a memory reduction method for specific object recognition with a strategy of skipping the distance calculation of feature vectors. The proposed method is characterized by the use of Bloomier filters, which are far memory efficient than hash tables, for the storage of feature vectors. The proposed method is evaluated based on experiments of planar and 3D specific object recognition in comparison to a method with a hash table.
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Keyword(in English) Bloomier filter / Bloom filter / Memory reduction / Specific object recognition / Local features
Paper # PRMU2009-56
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Conference Information
Committee PRMU
Conference Date 2009/6/11(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Memory Reduction with Bloomier Filters for Specific Object Recognition
Sub Title (in English)
Keyword(1) Bloomier filter
Keyword(2) Bloom filter
Keyword(3) Memory reduction
Keyword(4) Specific object recognition
Keyword(5) Local features
1st Author's Name Katsufumi INOUE
1st Author's Affiliation Graduate School of Engineering, Osaka Prefecture University()
2nd Author's Name Koichi KISE
2nd Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
Date 2009-06-19
Paper # PRMU2009-56
Volume (vol) vol.109
Number (no) 88
Page pp.pp.-
#Pages 6
Date of Issue