Volume 13 Number 5
October 2016
Article Contents
Santosh Kumar Vipparthi and ShyamKrishna Nagar. Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System. International Journal of Automation and Computing, vol. 13, no. 5, pp. 457-467, 2016. doi: 10.1007/s11633-016-0978-2
Cite as: Santosh Kumar Vipparthi and ShyamKrishna Nagar. Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System. International Journal of Automation and Computing, vol. 13, no. 5, pp. 457-467, 2016.

# Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System

Author Biography:
• Shyam Krishna Nagar received the Ph. D. degree from Department of Electrical Engineering, Indian Institute of Technology Roorkee, India in 1991. He is currently working as a professor at Department of Electrical Engineering, Indian Institute of Technology, Banaras Hindu University, India. His research interests include image processing, content-based image retrieval, digital control systems and model order reduction. E-mail: sknagar.eee@iitbhu.ac.in

• Corresponding author: Santosh Kumar Vipparthi received the B. Eng. degree in electrical and electronics engineering from Andhra University, India in 2007, the M. Eng. degree in systems engineering from the Indian Institute of Technology, India in 2010, where he is currently a Ph. D. degree candidate at Department of Electrical Engineering. Currently, he is working as an assistant professor at Department of Computer Science and Engineering, Malaviya National Institute of Technology, India. His research interests include image processing, content-based image retrieval and object tracking. E-mail: santu155@gmail.com (Corresponding author) ORCID iD: 0000-0002-5672-3537
• Accepted: 2014-12-04
• Published Online: 2016-07-25
• This paper presents a new feature descriptor, namely local extreme complete trio pattern (LECTP) for image retrieval application. The LECTP extracts complete extreme to minimal edge information in all possible directions using trio values. The LECTP integrates the local extreme sign trio patterns (LESTP) with magnitude local operator (MLOP) for image retrieval. The performance of the LECTP is tested by conducting three experiments on Corel-5 000, Corel-10 000 and MIT-VisTex color databases, respectively. The results after investigation show a significant improvement in terms of average retrieval precision (ARP) and average retrieval rate (ARR) as compared to the other state-of-the art techniques in content based image retrieval (CBIR).
•  [1] Y. Rui, T. S. Huang, S. F. Chang. Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39-62, 1999.  doi: 10.1006/jvci.1999.0413 [2] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, 2000.  doi: 10.1109/34.895972 [3] M. Kokare, B. N. Chatterji, P. K. Biswas. A survey on current content based image retrieval methods. IETE Journal of Research, vol. 48, no. 3-4, pp. 261-271, 2002.  doi: 10.1080/03772063.2002.11416285 [4] Y. Liu, D. S. Zhang, G. J. Lu, W. Y. Ma. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, vol. 40, no. 1, pp. 262-282, 2007.  doi: 10.1016/j.patcog.2006.04.045 [5] M. A. Stricker, M. Oreng. Similarity of color images. In Proceedings of the SPIE, Storage and Retrieval for Image and Video Databases Ⅲ, SPIE, San Jose, USA, pp. 381-392, 1995. [6] G. Pass, R. Zabih, J. Miller. Comparing images using color coherence vectors. In Proceedings of the 4th ACM International Conference on Multimedia, ACM, Boston, USA, pp. 65-73, 1997. [7] M. J. Swain, D. H. Ballar. Indexing via color histograms. In Proceedings of the 3rd International Conference on Computer Vision, IEEE, Osaka, Japan, pp. 390-393, 1990. [8] J. Huang, S. R. Kumar, M. Mitra. Combining supervised learning with color correlograms for content-based image retrieval. In Proceedings of the 5th ACM International Conference on Multimedia, ACM, Seattle, USA, pp. 325-334, 1997. [9] J. R. Smith, S. F. Chang. Automated binary texture feature sets for image retrieval. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing, IEEE, Atlanta, USA, pp. 2239-2242, 1996. [10] A. Ahmadian, A. Mostafa. An efficient texture classification algorithm using Gabor wavelet. In Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Cancun, Mexico, pp. 930-933, 2003. [11] H. A. Moghaddam, T. T. Khajoie, A. H. Rouhi, M. S. Tarzjan. Wavelet Correlogram: A new approach for image indexing and retrieval. Pattern Recognition, vol. 38, no. 12, pp. 2506-2518, 2005.  doi: 10.1016/j.patcog.2005.05.010 [12] A. B. Gonde, R. P. Maheshwari, R. Balasubramanian. Content-based image retrieval using colour feature and colour bit planes. International Journal of Signal and Imaging Systems Engineering, vol. 3, no. 2, pp. 105-115, 2010.  doi: 10.1504/IJSISE.2010.034999 [13] M. Kokare, P. K. Biswas, B. N. Chatterji. Texture image retrieval using rotated wavelet filters. Pattern Recognition Letters, vol. 28, no. 10, pp. 1240-1249, 2007.  doi: 10.1016/j.patrec.2007.02.006 [14] M. Kokare, P. K. Biswas, B. N. Chatterji. Texture image retrieval using new rotated complex wavelet filters. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 35, no. 6, pp. 1168-1178, 2005.  doi: 10.1109/TSMCB.2005.850176 [15] M. Subrahmanyam, R. P. Maheshwari, R. Balasubramanian. A correlogram algorithm for image indexing and retrieval using wavelet and rotated wavelet filters. Interna tional Journal of Signal and Imaging Systems Engineering, vol. 4, no. 1, pp. 27-34 2011.  doi: 10.1504/IJSISE.2011.039182 [16] T. Ojala, M. Pietikäinen, D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.  doi: 10.1016/0031-3203(95)00067-4 [17] T. Ojala, M. Pietikainen, T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002.  doi: 10.1109/TPAMI.2002.1017623 [18] M. Pietikäinen, T. Ojala, Z. Xu. Rotation-invariant texture classification using feature distributions. Pattern Recognition, vol. 33, no. 1, pp. 43-52, 2000.  doi: 10.1016/S0031-3203(99)00032-1 [19] Z. H. Guo, D. Zhang, D. Zhang. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1657-1663, 2010.  doi: 10.1109/TIP.2010.2044957 [20] M. Subrahmanyam, Q. M. Jonathan, R. P. Maheshwari, R. Balasubramanian. Modified color motif co-occurrence matrix for image indexing and retrieval. Computers and Electrical Engineering, vol. 39, no. 3, pp. 762-774, 2013.  doi: 10.1016/j.compeleceng.2012.11.023 [21] S. Murala, R. P. Maheshwari, R. Balasubramanian. Directional binary wavelet patterns for biomedical image indexing and retrieval. Journal of Medical Systems, vol. 36, no. 5, pp. 2865-2879, 2012.  doi: 10.1007/s10916-011-9764-4 [22] S. Murala, R. P. Maheshwari, R. Balasubramanian. Local tetra patterns: A new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874-2886, 2012.  doi: 10.1109/TIP.2012.2188809 [23] M. Subrahmanyam, R. P. Maheshwari, R. Balasubramanian. Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking. Signal Processing, vol. 92, no. 6, pp. 1467-1479, 2012.  doi: 10.1016/j.sigpro.2011.12.005 [24] M. Subrahmanyam, R. P. Maheshwari, R. Balasubramanian. Directional local extrema patterns: A new descriptor for content based image retrieval. International Journal of Multimedia Information Retrieval, vol. 1, no. 3, pp. 191-203, 2012.  doi: 10.1007/s13735-012-0008-2 [25] S. Vipparthi, S. Nagar. Directional local ternary patterns for multimedia image indexing and retrieval. International Journal of Signal and Imaging Systems Engineering, to be published. [26] S. K. Vipparthi, S. K. Nagar. Directional local quinary patterns: A new feature descriptor for image indexing and retrieval. In Proceedings of the International Conference on Advanced Computing, Networking, and Informatics, Raipur, India, pp. 837-844, 2014. [27] S. K. Vipparthi, S. K. Nagar. Multi-joint histogram based modelling for image indexing and retrieval. Computers and Electrical Engineering, vol. 40, no. 8, pp. 163-173, 2014.  doi: 10.1016/j.compeleceng.2014.04.018 [28] S. K. Vipparthi, S. K. Nagar. Expert image retrieval system using directional local motif XOR patterns. Expert Systems with Applications, vol. 41, no. 17, pp. 8016-8026, 2014.  doi: 10.1016/j.eswa.2014.07.001 [29] S. K. Vipparthi, S. K. Nagar. Integration of color and local derivative pattern features for content-based image indexing and retrieval. Journal of the Institution of Engineers (India): Series B, vol. 96, no. 3, pp. 251-263, 2014. [30] V. Takala, T. Ahonen, M. Pietikainen. Block-based methods for image retrieval using local binary patterns. In Proceedings of the 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, pp. 882-891, 2005. [31] M. Heikkil, M. Pietikäinen, C. Schmid. Description of interest regions with local binary patterns. Pattern Recognition, vol. 42, no. 3, pp. 425-436, 2009.  doi: 10.1016/j.patcog.2008.08.014 [32] X. Y. Tan, B. Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635-1650, 2010.  doi: 10.1109/TIP.2010.2042645 [33] Y. Koda, I. Kanaya, K. Sato. Modeling real objects for Kansei-based shape retrieval. International Journal of Automation and Computing, vol. 4, no. 1, pp. 14-17, 2007.  doi: 10.1007/s11633-007-0014-7 [34] G. Sahoo, K. Tapas, B. L. Raina, C. M. Bhatia. Text extraction and enhancement of binary images using cellular automata. International Journal of Automation and Computing, vol. 6, no. 3, pp. 254-260, 2009.  doi: 10.1007/s11633-009-0254-9 [35] Y. Z. Lu. A novel face recognition algorithm for distinguishing faces with various angles. International Journal of Automation and Computing, vol. 5, no. 2, pp. 193-197, 2008.  doi: 10.1007/s11633-008-0193-x [36] Corel Database, [Online], Available: http://wang.ist.psu.edu/docs/related.shtml. [37] MIT Vision and Modeling Group, Vision Texture, [Online], Available: http://vismod.www.media.mit.edu.
•  [1] Bing-Xing Wu, Suat Utku Ay, Ahmed Abdel-Rahim. Pedestrian Height Estimation and 3D Reconstruction Using Pixel-resolution Mapping Method Without Special Patterns . International Journal of Automation and Computing, 2019, 16(4): 449-461.  doi: 10.1007/s11633-019-1170-2 [2] Shui-Guang Tong, Yuan-Yuan Huang, Zhe-Ming Tong. A Robust Face Recognition Method Combining LBP with Multi-mirror Symmetry for Images with Various Face Interferences . International Journal of Automation and Computing, 2019, 16(5): 671-682.  doi: 10.1007/s11633-018-1153-8 [3] Tian-Xiang Zhang, Jin-Ya Su, Cun-Jia Liu, Wen-Hua Chen. Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture . International Journal of Automation and Computing, 2019, 16(1): 16-26.  doi: 10.1007/s11633-018-1143-x [4] Jia Sun, Peng Wang, Yong-Kang Luo, Gao-Ming Hao, Hong Qiao. Precision Work-piece Detection and Measurement Combining Top-down and Bottom-up Saliency . International Journal of Automation and Computing, 2018, 15(4): 417-430.  doi: 10.1007/s11633-018-1123-1 [5] Hossam Hakeem. Layered Software Patterns for Data Analysis in Big Data Environment . International Journal of Automation and Computing, 2017, 14(6): 650-660.  doi: 10.1007/s11633-016-1043-x [6] Kai Ma,  Liang Li,  Jie Yang,  Zhi-Xin Liu,  Xin-Bin Li,  Xin-Ping Guan. Bandwidth Allocation with Minimum Rate Constraints in Cluster-based Femtocell Networks . International Journal of Automation and Computing, 2015, 12(1): 77-82.  doi: 10.1007/s11633-014-0843-0 [7] Rong-Min Cao,  Zhong-Sheng Hou,  Hui-Xing Zhou. Data-driven Nonparametric Model Adaptive Precision Control for Linear Servo Systems . International Journal of Automation and Computing, 2014, 11(5): 517-526.  doi: 10.1007/s11633-014-0834-1 [8] Fan Guo, Jin Tang, Zi-Xing Cai. Image Dehazing Based on Haziness Analysis . International Journal of Automation and Computing, 2014, 11(1): 78-86.  doi: 10.1007/s11633-014-0768-7 [9] Li Wang,  Rui-Feng Li,  Ke Wang,  Jian Chen. Feature Representation for Facial Expression Recognition Based on FACS and LBP . International Journal of Automation and Computing, 2014, 11(5): 459-468.  doi: 10.1007/s11633-014-0835-0 [10] Muhammad Ilyas Menhas, Ling Wang, Min-Rui Fei, Cheng-Xi Ma. Coordinated Controller Tuning of a Boiler Turbine Unit with New Binary Particle Swarm Optimization Algorithm . International Journal of Automation and Computing, 2011, 8(2): 185-192.  doi: 10.1007/s11633-011-0572-6 [11] Chang-Jiang Zhang, Bo Yang. A Novel Nonlinear Algorithm for Typhoon Cloud Image Enhancement . International Journal of Automation and Computing, 2011, 8(2): 161-169.  doi: 10.1007/s11633-011-0569-1 [12] Zhong-Liang Pan, Ling Chen, Guang-Zhao Zhang. Cultural Algorithm for Minimization of Binary Decision Diagram and Its Application in Crosstalk Fault Detection . International Journal of Automation and Computing, 2010, 7(1): 70-77.  doi: 10.1007/s11633-010-0070-2 [13] Jing Sun,  Ying-Jie Xing. An Effective Image Retrieval Mechanism Using Family-based Spatial Consistency Filtration with Object Region . International Journal of Automation and Computing, 2010, 7(1): 23-30.  doi: 10.1007/s11633-010-0023-9 [14] G. Sahoo, Tapas Kumar, B. L. Rains, C. M. Bhatia. Text Extraction and Enhancement of Binary Images Using Cellular Automata . International Journal of Automation and Computing, 2009, 6(3): 254-260.  doi: 10.1007/s11633-009-0254-9 [15] Spyridon K. Gardikiotis,  Nicos Malevris. A Two-folded Impact Analysis of Schema Changes on Database Applications . International Journal of Automation and Computing, 2009, 6(2): 109-123.  doi: 10.1007/s11633-009-0109-4 [16] Yukiko Kenmochi, Lilian Buzer, Akihiro Sugimoto, Ikuko Shimizu. Discrete Plane Segmentation and Estimation from a Point Cloud Using Local Geometric Patterns . International Journal of Automation and Computing, 2008, 5(3): 246-256.  doi: 10.1007/s11633-008-0246-1 [17] Ying Weng,  Jianmin Jiang. Real-time and Automatic Close-up Retrieval from Compressed Videos . International Journal of Automation and Computing, 2008, 5(2): 198-201.  doi: 10.1007/s11633-008-0198-5 [18] Manoj Kumar, A. K. Verma, A. Srividya. Analyzing Effect of Demand Rate on Safety of Systems with Periodic Proof-tests . International Journal of Automation and Computing, 2007, 4(4): 335-341.  doi: 10.1007/s11633-007-0335-6 [19] Shang-Ming Zhou, John Q. Can, Li-Da Xu, Robert John. Interactive Image Enhancement by Fuzzy Relaxation . International Journal of Automation and Computing, 2007, 4(3): 229-235.  doi: 10.1007/s11633-007-0229-7 [20] Yukihiro Koda, Ichi Kanaya, Kosuke Sato. Modeling Real Objects for Kansei-based Shape Retrieval . International Journal of Automation and Computing, 2007, 4(1): 14-17.  doi: 10.1007/s11633-007-0014-7
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures (14)

## Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System

• ###### Corresponding author:Santosh Kumar Vipparthi received the B. Eng. degree in electrical and electronics engineering from Andhra University, India in 2007, the M. Eng. degree in systems engineering from the Indian Institute of Technology, India in 2010, where he is currently a Ph. D. degree candidate at Department of Electrical Engineering. Currently, he is working as an assistant professor at Department of Computer Science and Engineering, Malaviya National Institute of Technology, India. His research interests include image processing, content-based image retrieval and object tracking. E-mail: santu155@gmail.com (Corresponding author) ORCID iD: 0000-0002-5672-3537

Abstract: This paper presents a new feature descriptor, namely local extreme complete trio pattern (LECTP) for image retrieval application. The LECTP extracts complete extreme to minimal edge information in all possible directions using trio values. The LECTP integrates the local extreme sign trio patterns (LESTP) with magnitude local operator (MLOP) for image retrieval. The performance of the LECTP is tested by conducting three experiments on Corel-5 000, Corel-10 000 and MIT-VisTex color databases, respectively. The results after investigation show a significant improvement in terms of average retrieval precision (ARP) and average retrieval rate (ARR) as compared to the other state-of-the art techniques in content based image retrieval (CBIR).

Santosh Kumar Vipparthi and ShyamKrishna Nagar. Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System. International Journal of Automation and Computing, vol. 13, no. 5, pp. 457-467, 2016. doi: 10.1007/s11633-016-0978-2
 Citation: Santosh Kumar Vipparthi and ShyamKrishna Nagar. Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System. International Journal of Automation and Computing, vol. 13, no. 5, pp. 457-467, 2016.
Reference (37)

/