Presentation | 2012-03-28 Content Based Image Retrieval using Pules Coupled Neural Netowrks Masato YONEKAWA, Hiroaki KUROKAWA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A Pulse Coupled Neural Network (PCNN) is a neural network composed of neurons firing in discretetime and those connections. The image processing using the PCNN has been proposed in many studies. Aligned the PCNN's neurons in two dimensional are associated an image's pixels for the image processing. The PCNN-Icon is a time series of a number of firing neurons. PCNN-Icons that are information of neurons firing pattern is used the image matching by comparing those similarity. We proposed the parameter adaptaion method for the image matching using the PCNN. Image matching using the PCNN can show good recognition rate with this parameter adaptation method. On the other hand, because of the retrieval for huge amount of digital image data, the Content Based Image Retrieval(CBIR) has been interest in recent years. Similarity of PCNN-Icons should be able to applicate for CBIR. In this study, We applied the image matching using PCNN to the CBIR. An efficiency of proposed method for CBIR was shown in the simulation results. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Pulse Coupled Neural Network / Content Based Image Retrieval |
Paper # | NLP2011-151 |
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Conference Information | |
Committee | NLP |
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Conference Date | 2012/3/20(1days) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Content Based Image Retrieval using Pules Coupled Neural Netowrks |
Sub Title (in English) | |
Keyword(1) | Pulse Coupled Neural Network |
Keyword(2) | Content Based Image Retrieval |
1st Author's Name | Masato YONEKAWA |
1st Author's Affiliation | Tokyo University of Technology() |
2nd Author's Name | Hiroaki KUROKAWA |
2nd Author's Affiliation | Tokyo University of Technology |
Date | 2012-03-28 |
Paper # | NLP2011-151 |
Volume (vol) | vol.111 |
Number (no) | 498 |
Page | pp.pp.- |
#Pages | 6 |
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