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Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

Nguyen-Khang Pham, Annie Morin, Patrick Gros
IRISA, Universite de Rennes I, Campus de Beaulieu, 35042 RENNES Cedex, France
In Computer Analysis of Images and Patterns, Vol. 5702 (2009), pp. 565-572.

@conference{pham2009accelerating,

   title={Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU},

   author={Pham, N.K. and Morin, A. and Gros, P.},

   booktitle={Computer Analysis of Images and Patterns},

   pages={565–572},

   year={2009},

   organization={Springer}

}

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We are interested in the intensive use of Factorial Correspondence Analysis (FCA) for large-scale content-based image retrieval. Factorial Correspondence Analysis, is a useful method for analyzing textual data, and we adapt it to images using the SIFT local descriptors. FCA is used to reduce dimensions and to limit the number of images to be considered during the search. Graphics Processing Units (GPU) are fast emerging as inexpensive parallel processors due to their high computation power and low price. The G80 family of Nvidia GPUs provides the CUDA programming model that treats the GPU as a SIMD processor array. We present two very fast algorithms on GPU for image retrieval using FCA: the first one is a parallel incremental algorithm for FCA and the second one is an extension of the filtering algorithm in our previous work for filtering step. Our implementation is able to scale up the FCA computation a factor of 30 compared to the CPU version. For retrieval tasks, the parallel version on GPU performs 10 times faster than the one on CPU. Retrieving images in a database of 1 million images is done in about 8 milliseconds.
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