International Journal of Intelligent Information Systems
Volume 3, Issue 2, April 2014, Pages: 13-18
Received: May 26, 2014;
Accepted: Jun. 13, 2014;
Published: Jun. 30, 2014
Views 3078 Downloads 165
Ömer Faruk Ertuğrul, Electrical and Electronics Engineering, Batman University, Batman, Turkey
Recent developments in image quality, data storage, and computational capacity have heightened the need for texture analysis in image process. To date various methods have been developed and introduced for assessing textures in images. One of the most popular texture analysis methods is the Texture Energy Measure (TEM) and it has been used for detecting edges, levels, waves, spots and ripples by employing predefined TEM masks to images. Despite several successful studies, TEM has a number of serious weaknesses in use. The major drawback is; the masks are predefined therefore they cannot be adapted to image. A new method, Adaptive Texture Energy Measure Method (aTEM), was offered to overcome this disadvantage of TEM by using adaptive masks by adjusting the contrast, sharpening and orientation angle of the mask. To assess the applicability of aTEM, it is compared with TEM. The accuracy of the classification of butterfly, flower seed and Brodatz datasets are 0.08, 0.3292 and 0.3343, respectively by TEM and 0.0053, 0.2417 and 0.3153, respectively by aTEM. The results of this study indicate that aTEM is a successful method for texture analysis.
Ömer Faruk Ertuğrul,
Adaptive Texture Energy Measure Method, International Journal of Intelligent Information Systems.
Vol. 3, No. 2,
2014, pp. 13-18.
Srinivasan, G. N., and G. Shobha. "Statistical Texture Analysis." Proceedings of World Academy of Science: Engineer-ing & Technology 48 (2008).
Tamura, Hideyuki, Shunji Mori, and Takashi Yamawaki. "Textural features cor-responding to visual perception." Systems, Man and Cybernetics, IEEE Transactions on 8.6 (1978): 460-473.
Laws, Kenneth Ivan. Textured Image Segmentation. No. USCIPI-940. University of Southern Califor-nia Los Angeles Image Processing INST, (1980).
Rachidi, Mouna, et al. "Application of Laws’ masks to bone texture analysis: An innovative image analysis tool in osteoporosis." Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on. IEEE, (2008): 1191-1194
Maillard, Philippe. "Comparing texture analysis methods through classification."Photogrammetric engineering and remote sensing 69.4 (2003): 357-368.
Malik, Jitendra, et al. "Contour and texture analysis for image segmentation."International journal of computer vision 43.1 (2001): 7-27.
Haralick, Robert M., Karthikeyan Shanmugam, and Its' Hak Dinstein. "Textural features for image classification." Systems, Man and Cybernetics, IEEE Transactions on 6 (1973): 610-621.
Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale and rotation inva-riant texture classification with local binary patterns." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.7 (2002): 971-987.
Ballard, Dana H., and Christopher M. Brown. Computer Vision. Prentice-Hall Inc., Englewood Cliffs, N.J., (1982): 166-194.
Lee, Dong-Cheon, and Toni Schenk. "Image segmentation from texture measurement." International Archives of Photogrammetry and Remote Sensing29 (1993): 195-195.
Wu, Chung-Ming, Yung-Chang Chen, and Kai-Sheng Hsieh. "Texture features for classification of ultrasonic liver images." Medical Imaging, IEEE Transactions on 11.2 (1992): 141-152.
Miller, Peter, and Sue Astley. "Classification of breast tissue by texture analysis." Image and Vision Computing 10.5 (1992): 277-282.
Christodoulou, Christina I., et al. "Texture-based classification of atherosclerotic carotid pla-ques." Medical Imaging, IEEE Transactions on 22.7 (2003): 902-912.
Ojala, Timo, Matti Pietikäinen, and Jarkko Nisula. "Determining composition of grain mixtures by texture classification based on feature distribu-tions."International Journal of Pattern Recognition and Artificial Intelligence 10.01 (1996): 73-82.
Rachidi, Mouna, et al. "Application of Laws’ masks to bone texture analysis: An innovative image analysis tool in osteoporo-sis." Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on. IEEE, (2008): 1191-1194
G. Lemaitre and M. Rodojevic “Texture segmentation: Co-occurrence matrix and Laws’ texture masks methods”, http://g.lemaitre58.free.fr/pdf/vibot/scene_segmentation_interpretation/cooccurencelaw.pdf
Pietikaeinen, Matti, Azriel Rosenfeld, and Larry S. Davis. Texture Classification Using Averages of Local Pattern Matches. No. TR-1098. Maryland Univ College Park Computer Vision Lab, (1981) and Proceedings of IEEE Computer. Society Conference on Pattern Recognition and Image Processing, (1982).
Pietikainen, M., Rosenfeld A., and Davis. L. S., "Experi-ments with texture classification using averages of local pattern matches." Systems, Man and Cybernetics, IEEE Transactions on 3 (1983): 421-426.
Ade, Frank. "Characterization of textures by ‘eigenfilters’." Signal Processing5.5 (1983): 451-457.
Unser, Michael. "Local linear transforms for texture measurements." Signal Processing 11.1 (1986): 61-79.
Vistnes, Richard. "Texture models and image measures for texture discrimination." International journal of computer vision 3.4 (1989): 313-336.
Kayci, L. "Erek Dağı (Van) Papilionoidea ve Hesperioidea ekolojisi ve faunası üzerine araştırmalar (Lepidoptera)." Priamus Suppl 6 (2007): 1-47.
Brodatz, Phil. Textures: a photographic album for artists and designers. Vol. 66. New York: Dover, (1966), http://www.ux.uis.no/~tranden/brodatz.html
Bigün, Josef. "Frequency and orientation sensitive texture measures using linear symmetry." Signal processing 29.1 (1992): 1-16.
M. Goldstein, “kn-Nearest Neighbor Classification”, IEEE Transactions on Information Theory, (1972), IT-18 (5):627-630.
Kaya, Y., Kayci, L., Tekin, R., & Ertuğrul, ÖF. Evaluation of texture features for automatic detecting butterfly species using extreme learning machine. Journal of Experimental & Theoretical Artificial Intelligence, (2014): 1-15.
Hiremath, P. S., and S. Shivashankar. "On a Texture Classification Scheme using Wavelet Decomposition.", Proceedings of the International Conference on Cognition and Recognition, 214-218