Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern
As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.
The Bureau of National Planning and Development, Indonesia, Biodiversity and Action Plan 2003-2020, Jakarta, 2003.
Groombridge, B. & Jenkins, M., World Atlas of Biodiversity. Earth’s Living Resources in the 21st Century. Berkeley University of California Press, 2002.
Valerina, F., Comparison of Local Binary Pattern and Fuzzy Local Binary Pattern for Tropical Medicinal Plant Extraction. Undergraduate Thesis, Departement of Computer Science, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Bogor, 2012. (Text in Indonesian)
Herdiyeni, Y. & Wahyuni, N.K.S., Mobile Application for Indonesian Medicinal Plants Identification using Fuzzy Local Binary Pattern and Fuzzy Color Histogram. International Conference on Advanced Computer Science and Information System, December 1st-2nd, 2012 Jakarta, Indonesia, 2012.
Laxmi, G.F., Optimization of Fuzzy Local Binary Pattern in Threshold and Operator Selection using Multi Objective Genetic Algorithm. Master Thesis, Departement of Computer Science, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Indonesia, 2012. (Text in Indonesian)
Petryniak, R., Analysis of Efficiency of Parallel Computing in Image Processing Task. Czasopismo Techniczne, pp. 185-193, 2008.
Iakovidis, D.K., Keramidas, E.G. & Maroulis, D., Fuzzy Local Binary Patterns for Ultrasound Texture Characterization, ICIAR, LNCS 5112, pp. 750-759, 2008.
Ahonen, T., Hadid, A. & Pietikainen, M., Soft Histogram for Local Binary Patterns. CCV 2004, LNCS 3021, pp. 469-481, 2004.
Nasir, A.F.A., Rahman, M.N.A. & Mamat, A.R., A Study of Image Processing in Agriculture Application under High Performance Computing Environment, International Journal of Computer Science and Telecommunications, 3, pp. 16-24, 2012.
Quinn, M.J., Parallel Programming in C with MPI and Open MP, McGraw-Hill Education, Singapore, 2004.
Grama, A., Gupta, A., Karypis, G. & Kumar, V., Introduction to Parallel Computing. Pearson Education Limited, England, 2003.
Prajapati, H.B. & Vij, S.K., Analytical Study of Parallel and Distributed Image, International Conference on Image Information Processing (ICIIP), November 3rd-5th, India, 2011.
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