Comparison of design and performance of snow cover computing on GPUs and multi-core processors
WSEAS Transactions on Information Science and Applications
Design and performance evaluation of snow cover computing on GPUs
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Fast organization of large photo collections using CUDA
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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Scene recognition has become a remarkable field in image processing area, and many methods have been proposed in recent years, in which the idea of extracting the scene gist from global features has been proved to have higher retrieval accuracy compared with many other methods. However, the process of extracting gist is heavily time-consuming and not suitable for real-time application. In this paper, the CUDA architecture is deployed to accelerate this process. The influences of data transfer and memory access pattern on performance have also been investigated. Furthermore, when all the gist descriptors are got, parallel image feature similarity comparison is implemented with the OpenMP architecture. Experiments illustrate obvious speedup compared with implementation using only CPU.