Inspection of Memory Reduction Methodsfor Specific Object Recognition—Approaches by quantization and Selection of Local Features
Language
Japanese
Type
Proceedings of Japanese Conferences
Authors
Takayuki HONDO, Koichi KISE
Title
Inspection of Memory Reduction Methodsfor Specific Object Recognition—Approaches by quantization and Selection of Local Features
Journal
IEICE Technical Report
Presentation number
PRMU2008-265
Reviewed
Not reviewed
Date
March 2009
Research fields
Pattern recognition
Key
Hondo2009_IEICEPRMU (id=1071)
Abstract
In case of conducting large-scale specific objects recognition using local features such as SIFT, the number of local features increases. Hence, reduction of the memory utilization is an important issue. In this report, we
attempt memory reduction with two approaches; one is a method using vector quantization, and the other is one using selection of local features. From experimental results, we confirm that good results could not be acquired by vector quantization. On the other hand, if we employ feature selection as the method, the recognition rate hardly
decreases with a database whose size is 10% of its original.
Note
2009-CVIM-166 (26)
Files
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