A GPU implementation of a structural-similarity-based aerial-image classification

Authors:
Rok Češnovar;Vladimir Risojević;Zdenka Babić;Tomaž Dobravec;Patricio Bulić
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia;Faculty of Electrical Engineering, University of Banja Luka, Banja Luka, Bosnia-Herzegovina;Faculty of Electrical Engineering, University of Banja Luka, Banja Luka, Bosnia-Herzegovina;Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Venue:
The Journal of Supercomputing
Year:
2013

Citing 13
Cited 0

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
A performance study of general-purpose applications on graphics processors using CUDA

Journal of Parallel and Distributed Computing
Structural similarity metrics for texture analysis and retrieval

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bag-of-visual-words and spatial extensions for land-use classification

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Gabor descriptors for aerial image classification

ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Parallel medical image reconstruction: from graphics processing units (GPU) to Grids

The Journal of Supercomputing
Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms

The Journal of Supercomputing
Cardiac simulation on multi-GPU platform

The Journal of Supercomputing
Aerial image classification using structural texture similarity

ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Empowering Visual Categorization With the GPU

IEEE Transactions on Multimedia
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing
Stencil computations on heterogeneous platforms for the Jacobi method: GPUs versus Cell BE

The Journal of Supercomputing
G-MSA - A GPU-based, fast and accurate algorithm for multiple sequence alignment

Journal of Parallel and Distributed Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

There is an increasing need for fast and efficient algorithms for the automatic analysis of remote-sensing images. In this paper we address the implementation of the semantic classification of aerial images with general-purpose graphics-processing units (GPGPUs). We propose the calculation of a local Gabor-based structural texture descriptor and a structural texture similarity metric combined with a nearest-neighbor classifier and image-to-class similarity on CUDA supported graphics-processing units. We first present the algorithm and then describe the GPU implementation and optimization with the CUDA programming model. We then evaluate the results of the algorithm on a dataset of aerial images and present the execution times for the sequential and parallel implementations of the whole algorithm as well as measurements only for the selected steps of the algorithm. We show that the algorithms for the image classification can be effectively implemented on the GPUs. In our case, the presented algorithm is around 39 times faster on the Tesla C1060 unit than on the Core i5 650 CPU, while keeping the same success rate of classification.