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International Journal of Latest Research in Science and Technology

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REGION-BASED APPROACHES AND DESCRIPTORS EXTRACTED FROM THE CO-OCCURRENCE MATRIX

Research Paper Open Access

International Journal of Latest Research in Science and Technology Vol.3 Issue 6, pp 192-200,Year 2014

REGION-BASED APPROACHES AND DESCRIPTORS EXTRACTED FROM THE CO-OCCURRENCE MATRIX

Loris Nanni,Shery Brahnam, Stefano Ghidoni, Emanuele Menegatti

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Received : 15 December 2014; Accepted : 22 December 2014 ; Published : 31 December 2014

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Article No. 10447
Abstract

Recently proposed texture descriptors extracted from the co-occurrence matrix across several datasets is surveyed and validated in this paper; moreover, two new methods for extracting features from the Gray Level Co-occurrence Matrix (GLCM) are proposed. The descriptors are extracted not only from the entire GLCM but also from subwindows. These texture descriptors are used to train a support vector machine. We also explore region-based approaches, which use different methods to divide each image into two different regions; different descriptors are extracted from each region. In this work methods based on saliency detection, edge detection, and wavelets are compared, and some of their fusions are reported as well. Region-based approaches are combined with different methods for extracting features from the GLCM and with three state-of-the-art descriptors: local ternary patterns, local phase quantization, and rotation invariant co-occurrence among adjacent local binary patterns. Experimental results show that the tested approaches improve performance of standard methods. The generality of the proposed descriptors is demonstrated on 15 datasets, and different statistical comparisons based on the Wilcoxon signed rank test are reported that confirm the goodness of the proposed approaches. Experiments show that the new methods for extracting features from the GLCM greatly improve the standard features that are typically extracted, and that the region-based approach boosts the performance of texture descriptors extracted from the whole image. The MATLAB source code of all the proposed approaches will be made available to the public at https://www.dei.unipd.it/node/2357.

Key Words   
Co-occurrence matrix; texture descriptors; support vector machine; ensemble; region-based
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References
  1. M. Coggins: 'A framework for texture analysis based on spatial filtering'. Ph.D. Thesis, Michigan State University, 1982.
  2. W. Zucker: "Towards a model of texture", Computer Graphics Image Processing, vol. 5, pp. 190-202, 1976.
  3. Sklansky: "Image segmentation and feature extraction", IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-8, pp. 237-247, 1978.
  4. G. Lowe: "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
  5. Bay, T. Tuytelaars, and L. V. Gool: "SURF: Speeded up robust features", European Conference on Computer Vision, vol. 1, pp. 404-417, 2006.
  6. Dalal, and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. 9th European Conference on Computer Vision, San Diego, CA, 2005,
  7. Mikolajczyk, and C. Schmid: "A performance evaluation of local descriptors", IEEE Trans Pattern Analysis Mach Intell, vol. 29, no. 10, pp. 1615-1630, 2005.
  8. Tuzel, F. Porikli, and P. Meer, "Region covariance: A fast descriptor for detection and classification," in Proc. 9th European Conference on Computer Vision, 2006, pp. 589-600.
  9. Wu, and R. Nevatia, "Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors," in Proc. IEEE International Conference on Computer Vision, 2005, pp. 90-97.
  10. M. Haralick, K. Shanmugam, and I. Dinstein: "Textural features for image classification", IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, no. 6, pp. 610-621, 1973.
  11. Ojala, M. Pietikäinen, and D. Harwood: "A comparative study of texture measures with classification based on featured distribution", Pattern Recognit Lett, vol. 29, no. 1, pp. 51-59, 1996.
  12. M. Haralick: "Statistical and structural approaches to texture", Proceedings of the IEEE, vol. 67, no. 5, pp. 786-804, 1979.
  13. Gelzinis, A. Verikas, and M. Bacauskiene: "Increasing the discrimination power of the co-occurrence matrix-based features", Pattern Recognit, vol. 40, no. 9, pp. 2367-2372. , 2007.
  14. Walker, P. Jackway, and D. Longstaff: "Genetic algorithm optimization of adaptive multi-scale GLCM features", International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 1, pp. 17-39, 2003.
  15. Chen, W. Chengdong, D. Chen, and W. Tan, "Scene classification based on gray level-gradient co-occurrence matrix in the neighborhood of interest points," in Proc. IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009) 2009, pp. 482-485.
  16. Mitrea, P. Mitrea, S. Nedevschi, R. Badea, M. Lupsor, M. Socaciu, A. Golea, C. Hagiu, and L. Ciobanu: "Abdominal tumor characterization and recognition using superior-order cooccurrence matrices, based on ultrasound images", Computational and Mathematical Methods in Medicine, vol., pp. 2012.
  17. Hu, "Unsupervised texture classification by combining multi-scale features and k-means classifier," in Proc. Chinese Conference on Pattern Recognition, 2009, pp. 1-5.
  18. Pacifici, and M. Chini, "Urban land-use multi-scale textural analysis," in Proc. IEEE International Geoscience and Remote Sensing Symposium, 2008, pp. 342-345
  19. Rakwatin, N. Longepe, O. Isoguchi, M. Shimada, and Y. Uryu, "Mapping tropical forest using ALOS PALSAR 50m resolution data with multiscale GLCM analysis," in Proc. IEEE International Geoscience and Remote Sensing Symposium, 2010, pp. 1234-1237
  20. -S. Vu, T. Nguyen, and C. Garcia: "Improving texture categorization with biologically-inspired filtering", Image and Vision Computing, vol. 32, no. 6-7, pp. 424-436, 2014.
  21. Abdesselam: "Improving local binary patterns techniques by using edge information", Lecture Notes on Software Engineering, vol. 1, no. 2, pp. 360-363, 2013.
  22. L. C. dos Santosa, M. Paci, L. Nanni, S. Brahnam, and J. Hyttinen: "Computer vision for virus image classification", Biosystems Engineering, vol., in press.
  23. Nanni, M. Paci, F. L. C. D. Santos, S. Brahnam, and J. Hyttinen, "Analysis of virus textures in transmission electron microscopy images," in Proc. KES Innovation in Medicine and Healthcare (InMed-14), San Sebastián, Spain, 2014,
  24. Nanni, M. Paci, F. L. C. dos Santosa, H. Skottoman, K. Juuti-Uusitalo, and J. Hyttinen: "image-based classification of maturation of human stem cell-derived retinal pigmented epithelium", Expert Systems with Applications, vol., submitted.
  25. Nanni, S. Ghidoni, and E. Menegatti: "A comparison of multi-scale approaches for extracting image descriptors from the co-occurrence matrix", Computer Communication & Collaboration, vol., submitted 2013.
  26. Nanni, S. Brahnam, S. Ghidoni, E. Menegatti, and T. Barrier: "Different approaches for extracting information from the co-occurrence matrix", PLoS ONE, vol. 8, no. 12, pp. 1-9, 2013.
  27. F. Costa, G. E. Humpire-Mamani, and A. J. M. Traina, "An Efficient Algorithm for Fractal Analysis of Textures," in Proc. IBGRAPI 2012 (XXV Conference on Graphics, Patterns and Images), Ouro Preto, Brazil, 2012, pp. 39-46
  28. Gadermayr, M. Liedlgruber, A. Uhl, and A. Vécsei, "Shape Curvature Histogram: A Shape Feature for Celiac Disease Diagnosis," in Proc. 3rd International MICCAI - MCV Workshop 2014, pp. 175-184.
  29. Mallat: "A theory for multiresolution signal decomposition", IEEE Trans Pattern Analysis Mach Intell, vol. 11, pp. 674-693, 1989.
  30. Hou, J. Harel, and C. Koch: "Image signature: highlighting sparse salient regions", IEEE Trans Pattern Analysis Mach Intell, vol. 34, no. 1, pp. 194-201, 2012.
  31. Tan, and B. Triggs: "Enhanced local texture feature sets for face recognition under difficult lighting conditions", Analysis and Modelling of Faces and Gestures, vol. LNCS 4778, pp. 168-182, 2007.
  32. Ojansivu, and J. Heikkila, "Blur insensitive texture classification using local phase quantization," in Proc. ICISP, 2008, pp. 236-243.
  33. Nosaka, and K. Fukui: "HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns.", Pattern Recognition in Bioinformatics, vol. 47, no. 7, pp. 2428-2436, 2014.
  34. N. Vapnik: The Nature of Statistical Learning Theory (Springer-Verlag, 1995. 1995).
  35. Tang, "Texture information in run-length matrices," in Proc. IEEE Transactions On Image Processing, 1998, pp. 1602-1609.
  36. Liao, T. Chen, and P. Chung: "A fast algorithm for multilevel thresholding", Journal of Information Science and Engineering, vol. 17, no. 5, pp. 713-727, 2001.
  37. Canny: "A computational approach to edge detection", IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
  38. J. Baddeley, and B. W. Tatler: "High frequency edges (but not contrast) predict where we fixate: A Bayesian system identification analysis", Vision research, vol. 46, no. 18, pp. 2824-2833, 2006.
  39. Heusch, Y. Rodriguez, and S. Marcel, "Local binary patterns as an image preprocessing for face authentication," in Proc. 7th International Conference on Automatic Face and Gesture Recognition (FGR 2006), Southampton 2006,
  40. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, "Pap-smear benchmark data for pattern classification," in Proc. Nature inspired Smart Information Systems (NiSIS), Albufeira, Portugal, 2005, pp. 1-9.
  41. V. Boland, and R. F. Murphy: "A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells", BioInformatics, vol. 17, no. 12, pp. 1213-1223, 2001.
  42. Yuan: "Video-based smoke detection with histogram sequence of LBP and LBPV pyramids", Fire Safety Journal, vol. 46, no. 3, pp. 132-139, 2011.
  43. Cruz-Roa, J. C. Caicedo, and F. A. González: "Visual pattern mining in histology image collections using bag of features", Artificial Intelligence in Medicine, vol., no. 52, pp. 91-106, 2011.
  44. B. Junior, A. Cardoso de Paiva, A. C. Silva, and A. C. Muniz de Oliveira: "Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM", Computers in Biology and Medicine, vol. 39, no. 12, pp. 1063-1072, 2009.
  45. Nanni, J.-Y. Shi, S. Brahnam, and A. Lumini: "Protein classification using texture descriptors extracted from the protein backbone image", Journal of Theoretical Biology, vol. 3, no. 7, pp. 1024-1032, 2010.
  46. Hamilton, R. Pantelic, K. Hanson, and R. D. Teasdale: "Fast automated cell phenotype classification", BMC Bioinformatics, vol., pp. 8-110, 2007.
  47. Borghesani, C. Grana, and R. Cucchiara: "Miniature illustrations retrieval and innovative interaction for digital illuminated manuscripts in multimedia systems", Multimedia Systems, vol. 20, pp. 65-79, 2014.
  48. Foggia, G. Percannella, A. Saggese, and M. Vento: "Pattern recognition in stained HEp-2 cells:  Where  are  we  now?", Pattern  Recognition, vol. 4, no. 7, pp. 2305-2314, 2014.
  49. Khan, S. Beigpour, J. van de Weijer, and M. Felsberg: "Painting-91: a large scale database for computational painting categorization", Machine Vision and Applications, vol. 25, no. 6, pp. 1385-1397, 2014.
  50. Casanova, J. Joaci de Mesquita, and O. M. Bruno: "Plant leaf identification using gabor wavelets," International Journal of Imaging Systems and Technology, vol. 19, no. 3, pp. 236 - 243, 2009.
  51. Fawcett: "ROC graphs: Notes and practical considerations for researchers", in Editor (Ed.)^(Eds.): 'Book ROC graphs: Notes and practical considerations for researchers' (HP Laboratories, 2004, edn.), pp.
  52. Demšar: "Statistical comparisons of classifiers over multiple data sets", Journal of Machine Learning Research, vol. 7 pp. 1-30, 2006.
To cite this article

Loris Nanni,Shery Brahnam, Stefano Ghidoni, Emanuele Menegatti , " Region-based Approaches And Descriptors Extracted From The Co-occurrence Matrix ", International Journal of Latest Research in Science and Technology . Vol. 3, Issue 6, pp 192-200 , 2014


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