Detail of Publication
| Text Language | Japanese |
|---|---|
| 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 or not | Not reviewed |
| Month & Year | March 2009 |
| 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) - Following file is available.
- Entry for BibTeX
@InCollection{HONDO2009, author = {Takayuki HONDO and Koichi KISE}, title = {Inspection of Memory Reduction Methodsfor Specific Object Recognition---Approaches by quantization and Selection of Local Features}, booktitle = {IEICE Technical Report}, year = 2009, month = mar, presenID = {PRMU2008-265} }