Implementation of a Fast Image Coding and Retrieval System Using a GPU
SenSIP Center and Industry Consortium, School of ECEE, Arizona State University, Tempe, AZ 85287-5706, USA
IEEE International Conference on Emerging Signal Processing Applications, 2012
@article{Sattigeri2012implementation,
title={Implementation of a Fast Image Coding and Retrieval System Using a GPU},
author={Sattigeri, Prasanna and Thiagarajan, Jayaraman J. and Ramamurthy, Karthikeyan N. and Spanias, Andreas},
year={2012}
}
Sparse coding of image patches is a compact but computationally expensive method of representing images. As part of our SenSIP consortium industry projects, we implement the Orthogonal Matching Pursuit algorithm using a single CUDA kernel on a GPU and sparse codes for image patches are obtained in parallel. Image-based "exact search" and "visually similar search" using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and achieve good retrieval performance.
January 5, 2012 by hgpu
