Vision-based rock-type classification of limestone using multi-class support vector machine

Applied Intelligence

Volumn 39, Issue 1, 2013, Pages 14-27

  Chatterjee, Snehamoy ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Feature selection; Genetic algorithm; Image classification; Support vector machine

Indexed keywords

CLASSIFICATION METHODS; COMPARATIVE STUDIES; LIMESTONE MINES; MULTI-CLASS SUPPORT VECTOR MACHINES; NEURAL NETWORK MODEL; OVERALL ACCURACIES; STRATIFIED RANDOM SAMPLING; TEXTURAL FEATURE;

FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE CLASSIFICATION; LIMESTONE; ROCKS;

SUPPORT VECTOR MACHINES;

EID: 84878892750     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-012-0391-7     Document Type: Article

References (54)

1. Donskoi E, Suthers S, Fradd S, Young J, Campbell J, Raynlyn T, Clout J (2007) Utilization of optical image analysis and automatic texture classification for iron ore particle characterization. Miner Eng 20(5):461-471
2. Perez CA, Estévez PA, Vera PA, Castillo LE, Aravena CM, Schulz DA, Medina LE (2011) Ore grade estimation by feature selection and voting using boundary detection in digital image analysis. Int J Miner Process 101(1-4):28-36
3. Kulatilake PHSW, Hudaverdi T, Wu Q (2012) New prediction models for mean particle size in rock blast fragmentation. Geotech Geolog Eng 30(3):665-684
4. Singh N, Singh TN, Tiwary A, Sarkar KM (2010) Textural identification of basaltic rock mass using image processing and neural network. Comput Geosci 14(2):301-310
5. Tessier J, Duchesne C, Bartolacci G (2007) A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts. Miner Eng 20(12):1129-1144
6. Remy N, Boucher A, Wu J (2009) Applied geostatistics with SGeMs: a users's guide. Cambridge University Press, Cambridge
7. Perez C, Casali A, Gonzalez G, Vallebuona G, Vargas R (1999) Lithological composition sensor based on digital image feature extraction, genetic selection of features and neural classification. In: International conference on information intelligence and systems (ICIIS'99), p 236
8. Casali G, Vallebuona C, Pérez G, Vargas R (2000) Lithological composition and ore grindability sensors, based on image analysis. In: Proc. of the XXI IMPC, Rome, p A1(9-16)
9. Singh V, Rao SM (2005) Application of image processing and radial basis neural network techniques for ore sorting and ore classification. Miner Eng 18:1412-1420
10. Lepisto L, Kunttu I, Visa A (2005) Rock image classification using color features in Gabor space. J Electron Imaging 14(4):28-36
11. Hunter GC, McDermott C, Miles NJ, Singh A, Scoble MJ (1990) A review of image analysis techniques for measuring blast fragmentation. Min Sci Technol 11:19-36
12. Wang W (2008) Rock particle image segmentation and systems in pattern recognition. In: Yin P-Y (ed) Pattern recognition techniques, technology and applications. InTech, Rijeka, pp 197-226
13. Salinas RA, Raff U, Farfan C (2005) Automated estimation of rock fragment distributions using computer vision and its application in mining. IEE Proc, Vis Image Signal Process 152(1):1-8
14. Petersen K, Aldrich C, Vandeventer JSJ (1998) Analysis of ore particles based on textural pattern recognition. Miner Eng 11:959-977
15. Kachanubal T, Udomhunsakul S (2008) Rock textures classification based on textural and spectral features. Int J Comput Intell 4:240-246
16. Lin CL, Yen YK, Miller JD (1993) Evaluation of a pc image- based on-line coarse particle size analyzer. In: Emerging computer techniques for the minerals industry, society for mining, metallurgy, and exploration, inc, Littleton, Colorado, pp 201-210
17. Steppe JM, Bauer KW, Rogers SK (1996) Integrated feature and architecture selection. IEEE Trans Neural Netw 7:1007-1014
18. Duda RO, Hart PE (2000) Pattern classification. Wiley-Interscience, New York
19. Chatterjee S, Bhattacherjee A, Samanta B, Pal SK (2008) Rock-type classification of an iron ore deposit using digital image analysis technique. Int J Min Miner Eng 1:22-46
20. Chatterjee S, Bhattacherjee A, Samanta B, Pal SK (2009) Image-based quality monitoring system of limestone ore grades. Comput Ind 61:391-408
21. Chen X (2003) An improved branch and bound algorithm for feature selection. Pattern Recognit Lett 24(12):1925-1933
22. Somol P, Pudil P, Kittler J (2004) Fast branch & bound algorithms for optimal feature selection. IEEE Trans Pattern Anal Mach Intell 26(7):900-912
23. Hong J, Cho S (2006) Efficient huge-scale feature selection with speciated genetic algorithm. Pattern Recognit Lett 27:143-150
24. Osma-Ruiz V, Godino-Llorente JI, Sáenz-Lechón N, Gómez-Vilda P (2007) An improved watershed algorithm based on efficient computation of shortest paths. Pattern Recognit 40(3):1078-1090
25. Gonzalez RC, Woods RE, Eddins SL (2004) Segmentation using the watershed transform. In: Digital image processing using MATLAB. Pearson Prentice-Hall, Upper Saddle River
26. Ng HF (2006) Automatic thresholding for defect detection. Pattern Recognit Lett 27(14):1644-1649
27. Solomon C, Breckon T (2011) Fundamentals of digital image processing: a practical approach with examples in Matlab, E-book. Willey, New York
28. Cuisenaire O (2006) Locally adaptable mathematical morphology using distance transformations. Pattern Recognit 39(3):405-416
29. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62-66
30. Cuevas E, Sención F, Zaldivar D, Pérez-Cisneros M, Sossa H (2012) A multi-threshold segmentation approach based on artificial bee colony optimization. Appl Intell 37(3):321-336
31. Saha S, Bandyopadhyay S (2011) Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach. Appl Intell 35(3):411-427
32. Haralick RM, Shapiro LG (1992) Computer and robot vision 1 & 2. Addison-Wesley, Reading
33. Chatterjee S, Bhattacherjee A (2011) Genetic Algorithms for feature selection of image analysis-based quality monitoring model: an application to an iron mine. Eng Appl Artif Intell 24(5):786-795
34. Huang YL, Chen DR, Jiang YR, Kuo SJ, Wu HK, Moon WK (2008) Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet Gynecol 32:565-572
35. Lefkaditis D, Tsirigotis G (2009) Morphological feature selection and neural classification for electronic components. J Eng Sci Technol Rev 2(1):151-156
36. McNitt-Gray MF, Huang HK, Sayre JW (1995) Feature selection in the pattern classification problem of digital chest radiograph segmentation. IEEE Trans Med Imaging 14(3):537-547
37. Zucker SW, Terzopoulos D (1980) Finding structure in co-occurrence matrices for texture analysis. Comput Graph Image Process 12:286-308
38. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786-804
39. Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172-179
40. Chu A, Sehgal CM, Greenleaf JF (1990) Use of gray value distribution of run lengths for texture analysis. Pattern Recognit Lett 11(6):415-420
41. Dasarathy BR, Holder EB (1991) Image characterizations based on joint gray-level run-length distributions. Pattern Recognit Lett 12:497-502
42. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
43. Dioşan L, Rogozan A, Pecuchet JP (2012) Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters. Appl Intell 36(2):280-294
44. Lee LH, Wan CH, Rajkumar R, Isa D (2012) An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization. Appl Intell 37(1):80-99
45. Rifkin R, Mukherjee S, Tamayo P, Ramaswamy S, Yeang CH, Angelo M, Reich M, Poggio T, Lander ES, Golub TR, Mesirov JP (2003) An analytical method for multi-class molecular cancer classification. SIAM Rev 45:706-723
46. Li Y, Zeng X (2010) Sequential multi-criteria feature selection algorithm based on agent genetic algorithm. Appl Intell 33(2):117-131
47. Vinh LT, Lee S, Park YT, d'Auriol BJ (2012) A novel feature selection method based on normalized mutual information. Appl Intell 37(1):100-120
48. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
49. Congdon CB (1995) A comparison of genetic algorithm and other machine learning systems on a complex classification task from common disease research. PhD thesis, Computer Science and Engineering Division, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
50. Ranawana R, Palade V (2005) MVGen - ensemble learning for MCS majority voting with a genetic algorithm. Internal Report, Oxford University Computing Laboratory
51. Chatterjee S (2006) Geostatistical and image-based quality control models for indian mineral industry. Unpublished PhD Thesis dissertation, IIT Kharagpur, India
52. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, New York
53. Kim K, Ahn H (2012) Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36(4):887-898
54. Nekoukar V, Beheshti MTH (2010) A local linear radial basis function neural network for financial time-series forecasting. Appl Intell 33(3):352-356