Color and texture analysis using emerging parallel architectures

Authors:
Francisco D Igual;Rafael Mayo;Timothy Dr Hartley;Ümit V Çatalyürek;Antonio Ruiz;Manuel Ujaldon
Affiliations:
Department of Computer Engineering and Computer Science, University Jaume I, Castellon, Spain. email: figual@icc.uji.es, mayo@icc.uji.es;Department of Computer Engineering and Computer Science, University Jaume I, Castellon, Spain. email: figual@icc.uji.es, mayo@icc.uji.es;Departments of Biomedical Informatics and Electrical & Computer Engineering, The Ohio State University, Columbus, OH, USA. email: hartleyt@bmi.osu.edu, umit@bmi.osu.edu;Departments of Biomedical Informatics and Electrical & Computer Engineering, The Ohio State University, Columbus, OH, USA. email: hartleyt@bmi.osu.edu, umit@bmi.osu.edu;Computer Architecture Department, University of Malaga, Malaga, Spain. email: aruiz@uma.es, ujaldon@uma.es;Computer Architecture Department, University of Malaga, Malaga, Spain. email: aruiz@uma.es, ujaldon@uma.es
Venue:
International Journal of High Performance Computing Applications
Year:
2011

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Abstract

While image texture is effective for use in pattern-recognition and image-analysis algorithms, textural features are time-consuming to calculate on standard CPUs. Therefore, we present novel implementations of textural-feature algorithms on graphics processors (GPUs), enabling fast color and texture analysis. Since different textural-feature calculations exhibit diverse characteristics, we focus on using general and algorithm-specific techniques to exploit the inherent parallelism and computational power of a GPU. Common operations required during the textural-feature pipeline range from streaming computations to recursive procedures, from arithmetically intensive transcendental functions to matrix operations. Some of these kernels are well-suited to GPUs, while others require considerable programming effort to fully exploit the memory hierarchy due to their memory-usage patterns. In this paper, different strategies for computing textural features on GPUs are compared with counterpart implementations on multicore CPUs, and experimental results show GPU results reaching a speedup of 500 times for certain operations.