Memory Reduction Method for Specific Object Recognition Based on Lossy Encoding of Feature Vectors with a Bloomier Filter
Language
English
Type
Proceedings of International Conferences
Authors
Katsufumi Inoue, Koichi Kise
Title
Memory Reduction Method for Specific Object Recognition Based on Lossy Encoding of Feature Vectors with a Bloomier Filter
Journal
Proceedings of the 2nd China-Japan-Korea Joint Workshop on Pattern Recognition (CJKPR2010)
Pages
pp.166-171
Reviewed
Reviewed
Date
November 2010
Research fields
Object Recognition, Pattern recognition
Key
Inoue2010_CJKPR (id=1168)
Abstract
Specific object recognition method with the nearest neighbor search of local features needs an immense
of memory storage to store them for distance calculation since the number of local features is very large. A way to
solve this problem is to skip the distance calculation. In this paper, we propose a memory reduction method with a
Bloomier filter which is far memory efficient than hash tables. From the experiments with 55 3D objects and 5,000
planar objects, the proposed method was successful to reduce the required memory for specific object recognition
compared with the method with a hash table.
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