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研究生:顏志平
研究生(外文):Chih-Ping Yen
論文名稱:利用直覺模糊集合為基礎之整體與局部特徵及其在彩色紋理分析之應用
論文名稱(外文):Global and Local Features Based on Intuitionistic Fuzzy Sets for Color Texture Analysis and Application
指導教授:范國清范國清引用關係鄧少華鄧少華引用關係
指導教授(外文):Kuo-Chin FanShao-Hua Deng
學位類別:博士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:128
中文關鍵詞:直覺模糊集合紋理分類模糊樣式直方圖模糊樣式頻譜資料鑑識
外文關鍵詞:intuitionistic fuzzy sets (IFSs)texture classificationfuzzy motif histogram (FMH)fuzzy motif spectrum (FMS)data forensics
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  • 下載下載:46
  • 收藏至我的研究室書目清單書目收藏:0
紋理分類技術在電腦視覺的應用上,扮演相當重要的角色,在過去幾年儘管已有許多這方面技術的提出,但克服因環境變動如旋轉及缩放所造成紋理分類不一致的現象,仍是最主要的課題。
基於「直覺模糊集合」(intuitionistic fuzzy sets, IFSs)我們提出嶄新的整體與局部特徵,此兩個特徵能描述3個像點間的微紋理結構及其統計資訊,這整體特徵稱為「模糊樣式直方圖」(fuzzy motif histogram, FMH),而局部特徵稱為「模糊樣式頻譜」(fuzzy motif spectrum, FMS);同時,我們也設計出系統架構與直覺模糊集合的相似度比對,透過實驗證明,我們提出的方法不僅有高準確率,並且對旋轉及缩放亦具強韌度。
此外,經實證發現,一些最新的紋理分類方法在利用直覺模糊集合為基礎之下,均較原來提昇準確率。最後,我們的方法也應用在彩色雷射印表機列印來源的鑑識研究,除證明極佳的辨識效果,並建議未來建立印表機列印紋理專屬資料庫的可行性,以提供資料鑑識與犯罪偵查的使用。

Texture classification plays an important role in computer vision and has a wide variety of applications. Many methods of color texture analysis have been developed over the years; however, a major problem is that textures in the real world are often not uniform owing to variations in rotation and scale.
In this thesis, we propose novel features at both the global and local levels—namely, the fuzzy motif histogram (FMH) and the fuzzy motif spectrum (FMS)—using statistics and microtexture information spread across three pixels (i.e., higher-order statistics). Thus, this method achieves texture classification based on the intuitionistic fuzzy sets (IFSs) theory. Furthermore, we offer a system framework and a similarity measure between two IFSs. By conducting many experiments, we explored the effectiveness of the proposed methods, as well as their robustness against image changes, such as changes in rotation and scale.
Additionally, it was found empirically that for texture classification, several state-of-the-art IFS-based methods always achieve higher accuracy than non-IFS-based methods. Finally, we used our proposed system for color laser print identification and conducted a feasibility study to examine the system’s potential for use in digitizing a subject-specific, laser print database as part of data forensics and crime investigation operations.

摘要 .................................................. I
Abstract ............................................. II
誌謝 .................................................. III
List of Figures ...................................... VI
List of Tables ....................................... IX
Chapter 1 Introduction ............................... 1
1.1 Motivation ....................................... 1
1.2 Challenges ....................................... 2
1.3 Review of Related Work ........................... 3
1.4 Main Contributions ............................... 12
1.5 Organization of This Thesis ...................... 13
Chapter 2 IFS-based Texture Classification ........... 14
2.1 The Proposed System Framework .................... 14
2.2 Intuitionistic Fuzzy Image Processing ............ 16
2.2.1 Color Model Selection .......................... 16
2.2.2 Intuitionistic Fuzzy Image ..................... 17
2.2.3 Intuitionistic Fuzzy Entropy (IFE) ............. 22
2.3 Global Feature Extraction and Similarity Matching 23
2.3.1 Fuzzy Motif Histogram (FMH) .................... 24
2.3.2 Similarity Measures Between Two FMHs ........... 36
2.3.3 Multiscale FMH ................................. 38
2.4 Local Feature Extraction and Similarity Matching . 40
2.4.1 Motif Unit (MU) Based on IFS ................... 41
2.4.2 Motif Unit Number (MUN) Based on IFS ........... 42
2.4.3 Fuzzy Motif Spectrum (FMS) ..................... 43
2.4.4 Multiscale FMS ................................. 47
2.5 The Integration of FMH and FMS Features .......... 47
2.6 Dimensional Reduction Using Principal Component Analysis (PCA) ................................................ 49
2.7 Estimation of Fuzzification Parameter T .......... 50
Chapter 3 Analysis on Color Texture Classification ... 52
3.1 Classification Methods and Experimental Database . 52
3.1.1 Classification Methods and Notations ........... 52
3.1.2 Classifier and Cross-Validation ................ 55
3.1.3 Colored Brodatz Texture (CBT) Database and Setups 55
3.2 Analysis on IFS-based and non-IFS-based Methods .. 60
3.3 Performance Experiment ........................... 62
3.3.1 Comparative Analysis of Texture Classification Methods ...................................................... 62
3.3.2 Comparative Analysis of Computational Time and Accuracy ............................................. 65
3.3.3 Comparative Analysis of Multi-Fold Cross-Validation ...................................................... 66
3.3.4 Analysis on Integration of Multiscale FMH and Multiscale FMS ....................................... 67
3.3.5 Comparative Analysis with and without image .. 68
3.3.6 Analysis on parameter of IFG ................. 70
3.4 Robustness Experiment ............................ 71
3.4.1 Analysis on Rotation ........................... 72
3.4.2 Analysis on Scale .............................. 77
Chapter 4 Application in Color Laser Print Identification ...................................................... 83
4.1 Scenario and Print Identification Processing ..... 83
4.2 Color Laser Print (CLP) Database and Setups ...... 86
4.2.1 Gathering Color Laser Print via Stereo Microscope ...................................................... 86
4.2.2 Experimental Setup ............................. 89
4.3 Comparative Analysis of Texture Classification Methods ...................................................... 92
Chapter 5 Conclusions and Future Work ................ 94
5.1 Conclusions ...................................... 94
5.2 Future Work ...................................... 94
Reference ............................................ 97
Appendix ............................................. 106

[1] K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87-96, 1986.
[2] T. Chaira and A. K. Ray, “A new measure using intuitionistic fuzzy set theory and its application to edge detection,” Applied Soft Computing, vol. 8, no. 2, pp. 919-927, 2008.
[3] T. Chaira, “A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set,” Applied Soft Computing, vol. 12, no. 4, pp. 1259-1266, 2012.
[4] P. Couto, M. Pagola, H. Bustince, E. Barranechea, and P. Melo-Pinto, “Image segmentation using A-IFS,” in Proc. of the 12th Int. Conf. on Information Processing and Management of Uncertainty, Malaga, pp. 1620-1627, 2008.
[5] Y. K. Dubey and M. M. Mushrif, “Segmentation of brain MR images using intuitionistic fuzzy clustering algorithm,” in Proc. of the 8th Indian Conf. on Computer Vision, Graphics and Image Processing. ACM, 2012.
[6] C. C. Yu, “The applications of content-based image and video processing for forensic science,” Published doctoral dissertation, Central Police University, Taoyuan, Taiwan, 2011.
[7] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002.
[8] Q. Yin, J. N. Kim, and L. Shen, “Rotation-invariant texture classification using circular Gabor wavelets,” Optical Engineering, vol. 48, no. 1, Jan. 2009.
[9] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, no. 3, pp. 425-436, 2009.
[10] Z. Guo, L. Zhang, and D. Zhang, “Rotation invariant texture classification using LBP variance (LBPV) with global matching,” Pattern Recognition, vol. 43, no. 3, pp. 706-719, 2010.
[11] J. Zhang, H. Zhao, and J. Liang, “Continuous rotation invariant local descriptors for texton dictionary-based texture classification,” Computer Vision and Image Understanding, vol. 117, no. 1, pp. 56-75, 2013.
[12] R. Maani, S. Kalra, and Y. H. Yang, “Rotation invariant local frequency descriptors for texture classification,” IEEE Trans. on Image Processing, vol. 22, no. 6, pp. 2409-2419, 2013.
[13] D. S. Lu, S. Hetrick, and E. Moran, “Land cover classification in a complex Urban-Rural landscape with QuickBird imagery,” Photogrammetric Engineering &; Remote Sensing, vol. 76, no. 10, pp. 1159-1168, 2010.
[14] L. Nanni, A. Lumini, and S. Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis,” Artificial Intelligence in Medicine, vol. 49, no. 2, pp. 117-125, 2010.
[15] M. Singh, S. Singh, and S. Gupta, “An information fusion based method for liver classification using texture analysis of ultrasound images,” Information Fusion, vol. 19, pp. 91-96, Sep. 2014.
[16] K. W. Tobin et al., “Automated feature generation in large-scale geospatial libraries for content-based indexing,” Photogrammetric Engineering &; Remote Sensing, vol. 72, no. 5, pp. 531-540, 2006.
[17] T. Liu, L. Zhang, P. Li, and H. Lin, “Remotely sensed image retrieval based on region-level semantic mining,” EURASIP Journal on Image and Video Processing, vol. 2012, no.4, pp. 1-11, 2012.
[18] J. Sklansky, “Image segmentation and feature extraction,” IEEE Trans. on Systems, Man and Cybernetics, vol. 8, no. 4, pp. 237-247, 1978.
[19] S. Arivazhagan and L. Ganesan, “Texture classification using wavelet transform,” Pattern Recognition Letters, vol. 24, pp. 1513-1521, 2003.
[20] M. Karabatak, M. C. Ince, and A. Sengür, “Wavelet domain association rules for efficient texture classification,” Applied Soft Computing, vol. 11, no. 1, pp. 32-38, 2011.
[21] L. J. Sheela and V. Shanthi, “A novel texture classification procedure by using association rules,” ITB Journal of Information and Communication Technology, vol. 2, no. 2, pp. 103-114, 2008.
[22] S. Selvan and S. Ramakrishnan, “SVD-based modeling for image texture classification using wavelet transformation,” IEEE Trans. on Image Processing, vol. 16, no. 11, pp. 2688-2696, Nov. 2007.
[23] S. F. Ershad, “Color texture classification approach based on combination of primitive pattern units and statistical features,” The International Journal of Multimedia &; Its Applications (IJMA), vol. 3, no. 3, Aug. 2011.
[24] X. Xie, “A review of recent advances in surface defect detection using texture analysis techniques,” Electronic Letters on Computer Vision and Image Analysis, vol. 7 no. 3, pp. 1-22, 2008.
[25] M. Varma and A. Zisserman, “Texture classification: are filter banks necessary?” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 691-698, 2003.
[26] T. Ojala and M. Pietikäinen, “Texture classification,” Retrieved 2 Jan. 2014, from University of Oulu, Machine Vision and Media Processing Unit website: http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_
COPIES/OJALA1/texclas.htm
[27] T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51-59, Jan. 1996.
[28] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” Analysis and Modelling of Faces and Gestures, in: Lecture Notes Computer Science, vol. 4778, pp. 168-182, 2007.
[29] L. Nanni, S. Brahnam, and A. Lumini, “A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states,” Expert Systems with Applications, vol. 37, no. 12, pp. 7888-7894, Dec. 2010.
[30] Z. H. Guo, L. Zhang, and D. Zhang, “Rotation invariant texture classification using LBP variance (LBPV) with global matching,” Pattern Recognition, vol. 43, no. 3, pp. 706-719, Mar. 2010.
[31] X. Qian, X. Hua, P. Chen, and L. Ke, “PLBP: An effective local binary patterns texture descriptor with pyramid representation,” Pattern Recognition, vol. 44, no. 10, pp. 2502-2515, Oct. 2011.
[32] S. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Trans. on Image Processing, vol. 18, no. 5, pp. 1107-1118, May 2009.
[33] B. Jun, T. Kim, and D. Kim, “A compact local binary pattern using maximization of mutual information for face analysis,” Pattern Recognition, vol. 44, no. 3, pp. 532-543, Mar. 2011.
[34] T. Jabid, M. H. Kabir, and O. Chae, “Local Directional Pattern (LDP) for face recognition,” in Proc. of the IEEE International Conference of Consumer Electronics, pp. 329-330, Jan. 2010.
[35] B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor,” IEEE Trans. on Image Processing, vol. 19, no. 2, pp. 533-544, Feb. 2010.
[36] F. M. Khellah, “Texture classification using dominant neighborhood structure,” IEEE Trans. on Image Processing, vol. 20, no. 11, pp. 3270- 3279, Nov. 2011.
[37] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, “Extended local binary patterns for texture classification,” Image and Vision Computing, vol. 30, no. 2, pp. 86-99, Feb. 2012.
[38] N. Shadkam and M. S. Helfroush, “Texture classification by using co-occurrences of Local Binary Patterns,” in Proc. of the 20th Iranian Conference on Electrical Engineering (ICEE), pp. 1442-1446, May 2012.
[39] Z. Li, G. Liu, Y. Yang, and J. You, “Scale- and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift,” IEEE Trans. on Image Processing, vol. 21, no. 4, pp. 2130-2140, Apr. 2012.
[40] Y. Zhao, D. S. Huang, and W. Jia, “Completed Local Binary Count for rotation invariant texture classification,” IEEE Trans. on Image Processing, vol. 21 no. 10, pp. 4492-4497, Oct. 2012.
[41] Y. Zhao, W. Jia, R. X. Hu, and H. Min, “Completed robust local binary pattern for texture classification,” Neurocomputing, vol. 106, pp. 68-76, Apr. 2013.
[42] S. Murala, R. P. Maheshwari, and R. Balasubramanian, “Local tetra patterns: A new feature descriptor for content-based image retrieval,” IEEE Trans. on Image Processing, vol. 21, no. 5, pp. 2874-2886, May 2012.
[43] F. Yuan, “Rotation and scale invariant local binary pattern based on high order directional derivatives for texture classification,” Digital Signal Processing, vol. 26, pp. 142-152, Mar. 2014.
[44] R. M. Harlick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. on Systems, Man and Cybernetics, vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
[45] R. M. Haralick, “Statistical and structural approaches to texture,” in Proc. of the IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
[46] F. R. D. Siqueira, W. R. Schwartz, and H. Pedrini, “Multi-scale graylevel co-occurrence matrices for texture description,” Neurocomputing, vol. 120, pp. 336-345, Nov. 2013.
[47] J. H. Kim, S. C. Kim, and T. J. Kang, “Fractal dimension co-occurrence matrix method for texture classification,” in Proc. of TENCON 2006 - IEEE Region 10 Conference, Nov. 2006.
[48] J. Mridula, K. Kumar, and D. Patra, “Combining GLCM features and Markov random field model for colour textured image segmentation,” in Proc. of the 2011 International Conference on Devices and Communications (ICDeCom), pp. 1-5, Feb. 2011.
[49] F. Mirzapour, H. Ghassemian, “Using GLCM and Gabor filters for classification of PAN images,” in Proc. of the 21st Iranian Conference on Electrical Engineering (ICEE), pp. 1-6, May 2013.
[50] A. Rampun, H. Strange, and R. Zwiggelaar, “Texture segmentation using different orientations of GLCM features,” in Proc. of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications, Jun. 2013.
[51] G. García, A. Tapia, and M. D. Blas, “Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution,” Computer Methods and Programs in Biomedicine, vol. 115, no. 1, pp. 11-19, Jun. 2014.
[52] B. Pradhan, U. Hagemann, M. S. Tehrany, and N. Prechtel, “An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image,” Computers &; Geosciences, vol. 63, pp. 34-43, Feb. 2014.
[53] C. C. Chang, Z. C. Shih, and C. W. Chang, “Feature-based 3D texture synthesis approach,” International Journal of Innovative Computing Information and Control, vol. 9, no. 3, pp. 1201-1210, Mar. 2013.
[54] J. L. Raheja, S. Kumar, and A. Chaudhary, “Fabric defect detection based on GLCM and Gabor filter: A comparison,” Optik - International Journal for Light and Electron Optics, vol. 124, no. 23, pp. 6469-6474, Dec. 2013.
[55] M. Naeimizaghiani, S. N. H. S. Abdullah, F. Pirahanslah, and B. Bataineh, “Character and object recognition based on global feature extraction,” Journal of Theoretical and Applied Information Technology, vol. 52, no.2, Jun. 2013.
[56] K. K. Benazir and Vijayakumar, “Fingerprint matching by extracting GLCM features,” IJCA Proceedings on International Conference and Workshop on Emerging Trends in Technology, pp. 30-34, Mar. 2012.
[57] J. R. Smith and S. F. Chang, “Tools and techniques for color image retrieval,” in IS&;T/SPIE Symposium on Electronic Imaging: Science and Technology - Storage and Retrieval for Image and Video Database IV, vol. 2670, pp. 426-237, Feb. 1996.
[58] Z. H. Zhang, W. H. Li, and B. Li, “An improving technique of color histogram in segmentation based image retrieval,” in Proc. of IEEE, 5th International Conference on Information Assurance and Security, vol. 2, pp. 381-384, Aug. 2009.
[59] I. K. Vlachos and G. D. Sergiadis, “Role of entropy in intuitionistic fuzzy contrast enhancement,” in Proc. of the 12th international Fuzzy Systems Association World Congress on Foundations of Fuzzy Logic and Soft Computing, pp. 104-113, 2007.
[60] C. H. Hu and P. P. Li, “Edge detection for hardwood seedlings leaves based on intuitionistic fuzzy set,” in Proc. of the 2013 International Conference on Information Technology and Applications (ITA), pp. 76-80, Nov. 2013.
[61] T. Chaira, “Intuitionistic fuzzy color clustering of medical images,” in Proc. of IEEE, World Congress on Nature &; Biologically Inspired Computing, pp. 736-741, Dec. 2009.
[62] K. C. Hung, “Applications of medical information: Using an enhanced likelihood measured approach based on intuitionistic fuzzy sets,” IIE Trans. on Healthcare Systems Engineering, vol. 2, no. 3, pp. 224-231, Sep. 2012.
[63] H. Davarzani and M. A. Khorheh, “A novel application of intuitionistic fuzzy sets theory in medical science: Bacillus colonies recognition,” Journal of Artificial Intelligence Research, vol. 2, no. 2, pp. 1-17, Jun. 2013.
[64] V. Khatibi and G. A. Montazer, “Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition,” Artificial Intelligence in Medicine, vol. 47, no. 1, pp. 43-52, Sep. 2009.
[65] S. K. De, R. Biswas, and A. R. Roy, “An application of intuitionistic fuzzy sets in medical diagnosis,” Fuzzy Sets and Systems, vol. 117, no. 2, pp. 209-213, Jan. 2001.
[66] S. M. Chen and Y. Randyanto, “A novel similarity measure between intuitionistic fuzzy sets and applications,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 7, Nov. 2013.
[67] H. W. Liu, “New similarity measures between intuitionistic fuzzy sets and between elements,” Mathematical and Computer Modelling, vol. 42, no. 1, pp. 61-70, Jul. 2005.
[68] Z. Z. Liang and P. F. Shi, “Similarity measures on intuitionistic fuzzy sets,” Pattern Recognition Letters, vol. 24, no. 15, pp. 2687-2693, Nov. 2003.
[69] K. T. Atanassov, G. Pasi, and R. Yager, “Intuitionistic fuzzy interpretations of multi-criteria multi-person and multi-measurement tool decision making,” International Journal of Systems Science, vol. 36, no. 14, pp. 859-868, Feb. 2005.
[70] H. W. Liu and G. J. Wang, “Multi-criteria decision-making methods based on intuitionistic fuzzy sets,” European Journal of Operational Research, vol. 179, no. 1, pp. 220-233, May 2007.
[71] M. H. Shu, C. H. Cheng, and J. R. Chang, “Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly,” Microelectronics Reliability, vol. 46, no. 12, pp. 2139-2148, Dec. 2006.
[72] D. F. Li, A note on “Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly’’, Microelectronics Reliability, vol. 48, no. 10, pp. 1741, Oct. 2008.
[73] A. Kharal, “Homeopathic drug selection using intuitionistic fuzzy sets,” Homeopathy, vol. 98, no. 1, pp. 35-39, Jan. 2009.
[74] Z. Wang, K. W. Li, and W. Wang, “An approach to multiattribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights,” Information Sciences, vol. 179, no. 17, pp. 3026-3040, Aug. 2009.
[75] T. Y. Chen and C. H. Li, “Determining objective weights with intuitionistic fuzzy entropy measures: a comparative analysis,” Information Sciences, vol. 180, no. 21, pp. 4207-4222, Nov. 2010.
[76] I. K. Vlachos and G. D. Sergiadis, “Intuitionistic fuzzy information: applications to pattern recognition,” Pattern Recognition Letters, vol. 28, no. 2, pp. 197-206, Jan. 2007.
[77] I. K. Vlachos, G. D. Sergiadis, “Intuitionistic Fuzzy Image Processing,” Encyclopedia of Artificial Intelligence, pp. 967-974, 2009.
[78] P. Rajarajeswari and N. Uma, “Advanced Fuzzy Intuitionistic Logic Techniques in Image Processing,” Indian Journal of Computational and Applied Mathematics, vol. 1, no. 1, pp. 9-20, Oct. 2012.
[79] M. Sugeno, “Fuzzy measures and fuzzy integrals: A survey,” In: Gupta, Saridis, and Gaines (eds), Fuzzy Automata and Decision Processes, pp. 89-102, 1977.
[80] R. R. Yager, “On the measure of fuzziness and negation. Part I: Membership in the unit interval,” International Journal of General Systems, vol. 5, no. 4, pp. 221-229, 1979.
[81] C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, pp. 379-423, Jul. 1948.
[82] A. D. Luca and S. Termini, “A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory,” Information and Control, vol. 20, no. 4, pp. 301-312, May 1972.
[83] A. Kaufmann, Introduction to the theory of fuzzy subsets, Academic Press, New York, 1975.
[84] P. Burillo, H. Bustince, “Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets,” Fuzzy Sets and Systems, vol. 78, no. 3, pp. 305-316, Mar. 1996.
[85] E. Szmidt and J. Kacprzyk, “Entropy for intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 118, pp. 467-477, 2001.
[86] N. Jhanwar, S. Chaudhuri, G. Seetharaman, and B. Zavidovique, “Content based image retrieval using motif co-occurrence matrix,” Image and Vision Computing, vol. 22, no. 14, pp. 1211-1220, Dec. 2004.
[87] M. B. Rao, B. P. Rao, and A. Govardhan, “CTDCIRS: Content based image retrieval system based on dominant color and texture features,” International Journal of Computer Applications, vol. 18, no. 6, pp. 0975-8887, Mar. 2011.
[88] K. N. Prakash and K. S. Prasad, “HSV color motif co-occurrence matrix for content based image retrieval,” International Journal of Computer Applications, vol. 48 no. 16, Jun. 2012.
[89] M. Subrahmanyam, Q. M. J. Wu, R. P. Maheshwari, and R. Balasubramanian, “Modified color motif co-occurrence matrix for image indexing and retrieval,” Computers &; Electrical Engineering, vol. 39, no. 3, pp. 762-774, Apr. 2013.
[90] A. Hafiane, S. Chaudhuri, G. Seetharaman, and B. Zavidovique, “Region-based CBIR in GIS with local space filling curves to spatial representation,” Pattern Recognition Letters, vol. 27, no. 4, pp. 259-267, Mar. 2006.
[91] J. R. Quinlan, “Simplifying decision trees,” International Journal of Man-Machine Studies, vol. 27, no. 3, pp. 221-234, Sep. 1987.
[92] E. Szmidt and J. Kacprzyk, “Distances between intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 114, no. 3, pp. 505-518, Sep. 2000.
[93] E. Szmidt and J. Kacprzyk, “A new concept of a similarity measure for intuitionistic fuzzy sets and its use in group decision making,” Modeling Decisions for Artificial Intelligence, in: Lecture Notes Computer Science, vol. 3558, pp. 272-282, 2005.
[94] D. F. Li and C. T. Cheng, “New similarity measures of intuitionistic fuzzy sets and application to pattern recognition,” Pattern Recognition Letters, vol. 23, no. 1, pp. 221-225, Jan. 2002.
[95] Z. Liang and P. Shi, “Similarity measures on intuitionistic fuzzy sets,” Pattern Recognition Letters, vol. 24, no. 15, pp. 2687-2693, Nov. 2003.
[96] H. B. Mitchell, “On the Dengfeng–Chuntian similarity measure and its application to pattern recognition,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3101-3104, Dec. 2003.
[97] W. L. Hung and M. S. Yang, “Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance,” Pattern Recognition Letters, vol. 25, no. 14, pp. 1603-1611, Oct. 2004.
[98] W. L. Hung and M. S. Yang, “Similarity measures of intuitionistic fuzzy sets based on Lp metric,” International Journal of Approximate Reasoning, vol. 46, no. 1, pp. 120-136, Sep. 2007.
[99] J. Ye, “Cosine similarity measures for intuitionistic fuzzy sets and their applications, Mathematical and Computer Modelling, vol. 53, no. 1, pp. 91-97, Jan. 2011.
[100] C. M. Hwang, M. S. Yang, W. L. Hung, and M.G. Lee, “A similarity measure of intuitionistic fuzzy sets based on Sugeno integral with its application to pattern recognition,” Information Sciences, vol. 189, pp. 93-109, Apr. 2012.
[101] P. Julian, T. Hung, and S. Lin, “On the Mitchell similarity measure and its application to pattern recognition,” Pattern Recognition Letters, vol. 33, no. 9, pp. 1219-1223, Jul. 2012.
[102] P. C. P. Yen, K. C. Fan, and H. C. J. Chao, “A new method for similarity measures for pattern recognition,” Applied Mathematical Modelling, vol. 37, no. 7, pp. 5335-5342, Apr. 2013.
[103] H. Hotelling, “Analysis of a complex of statistical variables into principal components,” Journal of Educational Psychology, vol. 24, no. 6, pp. 417-441 and 498-520, Sep. 1933.
[104] M. A. Hoang, J. M. Geusebroek, and A. W. M. Smeulders, “Color texture measurement and segmentation,” Signal Processing, vol. 85, no. 2, pp. 265-275, 2005.
[105] P. A. Devijver, J. Kittler, Pattern Recognition: a statistical approach, Prentice-Hall, London, 1982.
[106] R. Esguerra, EFF's "Yellow Dots of Mystery" on instructables, Retrieved 3 Mar. 2014, from Electronic Frontier Foundation website: https://www.
eff.org/deeplinks/2008/10/effs-yellow-dots-mystery-instructables
[107] Halftone, Retrieved 4 May 2014, from Wikipedia website: http://en.wikipedia.org/wiki/Halftone
[108] Z. H. Lian, “Color laser print identification,” Published master’s thesis, Central Police University, Taoyuan, Taiwan, 2009.

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