SIFT & SURF on GPUs for Feature Extraction
Analysis on WANG Dataset for CBIR
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CBIR is required since large image databases are used both in scientific and medical domains and in the vast advertising/marketing domain.Since, images are a complex data to handle as they are composed of matrices and vectors of data and also due to multithread execution of algorithms, programmability and low cost, image processing becomes an appropriate field of achieving parallelism. GPU is a powerful graphics engine and a highly parallel programmable processor having better efficiency and high speed that overshadows CPU. A Content Based Image Retrieval system is implemented and tested on be...
CBIR is required since large image databases are used both in scientific and medical domains and in the vast advertising/marketing domain.Since, images are a complex data to handle as they are composed of matrices and vectors of data and also due to multithread execution of algorithms, programmability and low cost, image processing becomes an appropriate field of achieving parallelism. GPU is a powerful graphics engine and a highly parallel programmable processor having better efficiency and high speed that overshadows CPU. A Content Based Image Retrieval system is implemented and tested on benchmark WANG database using suitable and efficient techniques so as to achieve efficient results. The feature extraction phase is parallelized on GPU processor to obtain the speed up. Sequential and parallel implementation of CBIR is done using OpenCV. SURF and SIFT algorithms when implemented on GPU show better performance for feature extraction and for retrieval of images. The execution time is noted and on comparison with its corresponding serial counterpart a considerable speed up has been achieved.
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