CBIR is the method of searching the digital images from an image database.
“Content-based” means that the search analyzes the contents of the image rather t
han the
metadata such as colours, shapes, textures, or any other information that can be derived
from the image itself. The GPU is a powerful graphics engine and a highly parallel
programmable processor having better efficiency and high speed that overshadows CPU. It
is used in high performance computing system. The implementation of GPU can be done
with CUDA C. Due to its highly parallel structure it is used in a number of real time
applications like image processing, computational fluid mechanics, medical imaging etc.
Graphical Processors Units (GPU) is more common in most image processing applications
due to multithread execution of algorithms, programmability and low cost. In this paper, we
are explaining the parallel implementation of CBIR with GPU. We have shown various
stages of CBIR with GPU results into better performance as well as speed ups. We have
given a review of various techniques that can be practised for high performance CBIR
stages with Graphics Processing Units.
%0 Generic
%1 kaur2014content
%A Kaur, Bhavneet
%A and Sonika Jindal,
%D 2014
%I ACEEE (A Computer division of IDES)
%K Based CUDA Content Graphics Image Processor Retrieval Unit
%T Content based Image Retrieval with Graphical Processing Unit
%U http://searchdl.org/public/conference/2014/ITC/91.pdf
%X CBIR is the method of searching the digital images from an image database.
“Content-based” means that the search analyzes the contents of the image rather t
han the
metadata such as colours, shapes, textures, or any other information that can be derived
from the image itself. The GPU is a powerful graphics engine and a highly parallel
programmable processor having better efficiency and high speed that overshadows CPU. It
is used in high performance computing system. The implementation of GPU can be done
with CUDA C. Due to its highly parallel structure it is used in a number of real time
applications like image processing, computational fluid mechanics, medical imaging etc.
Graphical Processors Units (GPU) is more common in most image processing applications
due to multithread execution of algorithms, programmability and low cost. In this paper, we
are explaining the parallel implementation of CBIR with GPU. We have shown various
stages of CBIR with GPU results into better performance as well as speed ups. We have
given a review of various techniques that can be practised for high performance CBIR
stages with Graphics Processing Units.
@conference{kaur2014content,
abstract = {CBIR is the method of searching the digital images from an image database.
“Content-based” means that the search analyzes the contents of the image rather t
han the
metadata such as colours, shapes, textures, or any other information that can be derived
from the image itself. The GPU is a powerful graphics engine and a highly parallel
programmable processor having better efficiency and high speed that overshadows CPU. It
is used in high performance computing system. The implementation of GPU can be done
with CUDA C. Due to its highly parallel structure it is used in a number of real time
applications like image processing, computational fluid mechanics, medical imaging etc.
Graphical Processors Units (GPU) is more common in most image processing applications
due to multithread execution of algorithms, programmability and low cost. In this paper, we
are explaining the parallel implementation of CBIR with GPU. We have shown various
stages of CBIR with GPU results into better performance as well as speed ups. We have
given a review of various techniques that can be practised for high performance CBIR
stages with Graphics Processing Units. },
added-at = {2014-03-24T05:45:21.000+0100},
author = {Kaur, Bhavneet and and Sonika Jindal},
biburl = {https://www.bibsonomy.org/bibtex/27fe0ebf11adf15c6ec1cec9dfca170fb/idescitation},
interhash = {a61ee58575e482eb6120f22b1ca1e520},
intrahash = {7fe0ebf11adf15c6ec1cec9dfca170fb},
keywords = {Based CUDA Content Graphics Image Processor Retrieval Unit},
organization = {Institute of Doctors Engineers and Scientists},
publisher = {ACEEE (A Computer division of IDES)},
timestamp = {2014-03-24T05:45:21.000+0100},
title = {Content based Image Retrieval with Graphical Processing Unit },
url = {http://searchdl.org/public/conference/2014/ITC/91.pdf},
year = 2014
}