Dual Transition Uniform Lbp Matrix for Efficient Image Retrieval
Автор: V.Vijaya Kumar, A. Srinivasa Rao, YK Sundara Krishna
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 8 vol.7, 2015 года.
Бесплатный доступ
Texture image retrieval plays a significant and important role in these days, especially in the era of big-data. The big-data is mainly represented by unstructured data like images, videos and messages etc. Efficient methods of image retrieval that reduces the complexity of the existing methods is need for the big-data era. The present paper proposes a new method of texture retrieval based on local binary pattern (LBP) approach. One of the main disadvantages of LBP is, it generates 256 different patterns on a 3x3 neighborhood and a method based on this for retrieval needs 256 comparisons which is very tedious and complex. The retrieval methods based on uniform LBP's which consists of 59 different patterns of LBP is also complex in nature. To overcome this, the present paper divided LBP into dual LBP's consisting four pixels. The present paper based on this dual LBP derived a 2-dimensional dual uniform LBP matrix (DULBPM) that contains only four entries. The texture image retrieval is performed using these four entries of DULBPM. The proposed method is evaluated on the animal fur, car, leaf and rubber textures.
Dimensionality, Big-data, Complexity, Local binary pattern, Dual uniform local binary matrix
Короткий адрес: https://sciup.org/15013899
IDR: 15013899
Список литературы Dual Transition Uniform Lbp Matrix for Efficient Image Retrieval
- Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B., 2011. Content-based image retrieval in radiology: current status and future directions. J. Digital Imag. 24, 208–222.
- Aigrain, P., Zhang, H., Petkovic, D., 1996. Content-based representation and retrieval of visual media: a state-of-the-art review. Multimedia Tools Appl. 3, 179–202.
- Mojsilovic, A., Rogowitz, B., 2001. Capturing image semantics with low-level descriptors. In: Proceedings of the IEEE International Conference on Image Processing, pp. 18–21.
- G. Marchionini, Information-seeking strategies of novices using a full-text electronic encyclopedia, J. Amer. Soc. Inform. Sci. 40 (1) (1989) 54–66.
- K. Ericsson, A. Lehmann, Expert and exceptional performance: Evidence of maximal adaptation to task constraints, Annu. Rev. Psychol. 47 (1) (1996), 273–305.
- T.Q. Lee, Y. Park, Y.T. Park, A time-based approach to effective recommender systems using implicit feedback, Expert Syst. Appl. 34 (4) (2008) 3055– 3062.
- S. Krug, Don't Make Me Think!, New Riders Publishing, 2006.
- A. Spink, R. Losee, Feedback in information retrieval, Annu. Rev. Inform. Sci. Technol. 31 (1996) 33–78.
- I. Lopatovska, I. Arapakis, Theories, methods and current research on emotions in library and information science, information retrieval and human– computer interaction, Inform. Process.Manag. 47 (4) (2011) 575–592.
- A. Oulasvirta, J. Hukkinen, B. Schwartz, When more is less: The paradox of choice in search engine use, 2009, pp. 516–523.
- B. Poblete, R. Baeza-Yates, Query-sets: Using implicit feedback and query patterns to organize Web documents, 2008, pp. 41–50.
- Y. Xu, D. Wang, Order effect in relevance judgment, J. Amer. Soc. Inform. Sci. Technol. 59 (8) (2008) 1264–1275.
- Z. Lei, L. Fuzong, Z. Bo, A CBIR method based on color-spatial feature, in: TENCON, Proceedings of the IEEE Region 10 Conference, 1999, pp. 166–169.
- H.B. Kekre, S.D. Thepade, Color traits transfer to grayscale images, in: IEEE Int. Conference on Emerging Trends in Engineering and Technology, ICETET, 2008.
- C.M. Pun, C.F. Wong, Fast and robust, color feature extraction for content-based image retrieval, Int. J. Adv. Comput.Technol. 3 (6) (2011).
- A. Vadivel, S. Sural, A.K. Majumdar, Human color perception in the HSV space and its application in histogram generation for image Retrieval2, in: Proc. SPIE, Color Imaging X: Processing, Hardcopy, and Applications, 2005, pp. 598–609.
- C.-R. Shyu, C. E. Brodley, A. C. Kak, A. Kosaka, A. M. Aisen, and L. S. Broderick, "Assert: A physician-in-the-loop content-based retrievalsystem for HRCT image databases," Comput. Vis. Image Understand.,vol. 75, no. 1–2, pp. 111–132, 1999.
- L. Zheng, A. Wetzel, J. Gilbertson, and M. Becich, "Design and analysis of a content-based pathology image retrieval system," IEEE Trans. Inf.Technol. Biomed., vol. 7, no. 4, pp. 249–255, Dec. 2003.
- L. Yang, O. Tuzel,W. Chen, P.Meer, G. Salaru, L.Goodell, and D. Foran, "Pathminer:Aweb-based tool for computer-assisted diagnostics in pathology," IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 3, pp. 291–299, May,2009.
- Liao, M.Law, Chung, "Dominant Local Binary Patterns for Texture Classification", IEEE Transactions on Image Processing, 2009; 18(5):1107–1118.
- G.Zhao, M.Pietik€ainen., "Dynamic texture recognition using local binary patterns with an application to facial expressions", IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2007; 29 (6):915–928.
- W.Zhang, S.Shan, W.Gao, X.Chen, H.Zhang, "Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition", 10th IEEE International Conference on Computer Vision (ICCV), 2005: 786-791.
- S.Zhang, H.Yao, SLiu, "Dynamic back ground modeling and subtraction using spatiotemporal local binary patterns", International Conference on image processing (ICIP), 2008: 1556–1559.
- Z.Guo, L.Zhang, D.Zhang, "Rotation in variant texture classification using LBP variance (LBPV) with global matching", Pattern Recognition, 2010: 43(3): 706–719.
- S.Xie, S.Shan, X.Chen, W.Gao, "V-LGBP: volume based local Gabor binary patterns for face representation and recognition", in: Proceedings of the 19th International Conference on Pattern Recognition (ICPR), 2008:1-4.
- S.Liao, X.Zhu, Z.Lei, L.Zhang, S.Z.Li, "Learning multi-scale block local binary patterns for face recognition", in: Proceedings of the International Conference on Biometrics (ICB), 2007; 828-837.
- Ojala T.; Pietikäinen M.; Mäenpää T.; "Multi resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns", IEEE Trans., 24(7), 2002, pp. 971-987.
- Li, Z.; Liu, G.; Yang, Y.; You, J.; "Scale- and rotation-invariant local binary pattern using scale-adaptive texton and sub uniform-based circular shift", IEEE Trans. Image Process, 21(4), 2012, pp. 2130–2140.
- Fathi, Abdolhossein, and Ahmad Reza Naghsh-Nilchi. "Noise tolerant local binary pattern operator for efficient texture analysis." Pattern Recognition Letters 33, no. 9 (2012): 1093-1100.
- Heikkila, Marko, Matti Pietikainen, and Cordelia Schmid. "Description of interest regions with local binary patterns." Pattern recognition 42, no. 3 (2009): 425-436.
- Liao, Shu, Max WK Law, and Albert CS Chung. "Dominant local binary patterns for texture classification." Image Processing, IEEE Transactions on 18, no. 5 (2009): 1107-1118.
- Murty, Gorti Satyanarayana, J. Sasi Kiran, and V. Vijaya Kumar. "Facial Expression Recognition Based on Features Derived From the Distinct LBP and GLCM." International Journal of Image, Graphics and Signal Processing (IJIGSP) 6, no. 2 (2014): 68.
- Nanni, Loris, Sheryl Brahnam, and Alessandra Lumini. "A simple method for improving local binary patterns by considering non-uniform patterns." Pattern Recognition 45, no. 10 (2012): 3844-3852.
- V.Vijaya Kumar, K.Srinivasa Reddy, V.V.Krishna, "Face recognition using prominent LBP model", International Journal of Applied Engineering Research (IJAER), Volume 10, No.2(2015), pp 4373-4384.
- Zhou, Hui, Runsheng Wang, and Cheng Wang. "A novel extended local-binary-pattern operator for texture analysis." Information Sciences 178, no. 22 (2008): 4314-4325.
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