پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 414 |
| تعداد مقالات | 5,478 |
| تعداد مشاهده مقاله | 6,010,552 |
| تعداد دریافت فایل اصل مقاله | 5,299,927 |
Fuzzy Content-Based Image Retrieval Speed-up Using the Multi-Agent Platform | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 1، دوره 54، شماره 1، شهریور 2022، صفحه 3-18 اصل مقاله (1.15 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2022.20649.5261 | ||
| نویسنده | ||
| monireh azimi hemat* | ||
| Department of Computer Engineering and Information Technology, Payame Noor University, Iran | ||
| چکیده | ||
| Parallelization is a technique that increases the speed of tasks by processing them simultaneously. Distributed multi-agent systems are one of the cases in which parallel processing techniques can be used. In this paper, first, the multi-agent model of a fuzzy content-based image retrieval system is designed to distribute it. Next, the corresponding parallel multi-agent model is designed. Afterwards, the parallel image retrieval system is implemented on reconfigurable hardware. This method is based on a multi-agent paradigm which is suitable for various parallelisms within applied problems. In this study, by using parallelism techniques, a method is presented that considerably decreases the image retrieval systems consumption time. This paper focuses on how to implement a fuzzy content-based image retrieval system in the form of a multi-agent model. Since reconfigurable hardware is appropriate to support software agents, I also show how these agents use the inherent parallelism of reconfigurable Hardware for parallel image retrieval and increase the speed of this new system greatly. The two sequential and parallel systems are tested on a data set containing 1000 images. The results signify the increase of almost 3-times speed for the proposed parallel system by software agents, and 400-times speed by hardware agents. In order to evaluate the retrieval efficiency of the proposed system compared to the previous works, two other image retrieval systems have been implemented and the efficiency and memory consumption of the systems have been compared. The results indicate better performance of the proposed system than other methods studied. | ||
| کلیدواژه‌ها | ||
| Image Retrieval؛ Multi-Agents؛ Parallelism؛ reconfigurable hardware | ||
| مراجع | ||
|
[1] Y.Rui and T.S.Huang, 1999, Image retrieval: current technique promising directions and open issuesî , Journal of Visual Communication and Image Representation, vol.10, pp.39-62.
[2] Y.Li, X.Wan and C.C.J.Kuo, 2001, Introduction to content-based image retrieval-overview of key techniquesî , in Image Database: Search and Retrieval of Digital Imagery, Edited by Bergman and Castelli, John Wiley & Sons.
[3] Ponomarev, A., Nalamwar, H. S., Babakov, I., Parkhi, C. S., & Buddhawar, G. (2016, February). Content-based image retrieval using color, texture and shape features. Key Engineering Materials, 685, 872–876
[4] Srivastava, P., & Khare, A. (2017, January). Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval. Journal of Visual Communication and Image Representation, 42, 78–103.
[5] Sajjad, M., Ullah, A., Ahmad, J., Abbas, N., Rho, S., & Baik, S. W. (2018, February). Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimedia Tools and Applications, 77(4), 4769–4789.
[6] Pavithra, L. K., & Sharmila, T. S. (2018, August). An efficient framework for image retrieval using olor, texture and edge features. Computers & Electrical Engineering, 70, 580–593.
[7] Pavithra, L. K., & Sree Sharmila, T. (2019, December). An efficient seed points selection approach in dominant color descriptors (DCD). Cluster Computing, 22(4), 1225–1240.
[8] Ashraf, R., Ahmed, M., Ahmad, U., Habib, M. A., Jabbar, S., & Naseer, K. (2020, April). MDCBIR-MF: Multimedia data for content-based image retrieval by using multiple features. Multimedia Tools and Applications, 79(13–14), 8553–8579.
[9] Zhao, M., Zhang, H., & Sun, J. (2016, July). A novel image retrieval method based on multi-trend structure descriptor. Journal of Visual Communication and Image Representation, 38, 73–81.
[10] Raza, A., Dawood, H., Dawood, H., Shabbir, S., Mehboob, R., & Banjar, A. (2018). Correlated primary visual texton histogram features for content base image retrieval. IEEE Access, 6, 46595–46616.
[11] S. Mezzoudj, A. Behloul, R. Seghir, Y. Saadna , A parallel content-based image retrieval system using spark and tachyon frameworks , Journal of King Saud University – Computer and Information Sciences 33 (2021) 141–149.
[12] A. Bagherjeiran, R. Vilalta, C. F. Eick, Content-Based Image Retrieval Through a Multi-Agent Meta-Learning Framework, Conference: Tools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on.
[13] Zhu, L., “Accelerating content-based image retrieval via GPUadaptive index structure”, The Scientific World Journal, Vol. 2014, (2014), 1–11.
[14] A.S. Akopov, L.A. Beklaryan, M. Thakur et al., Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimisation, Knowledge-Based Systems (2019).
[15] Y. Li, X. Yang, J. Wu, H. Sun , X Guo, L. Zhou, , Discrete-event simulations for metro train operation under emergencies: A multi-agent based model with parallel computing, Physica A 573 (2021) 125964. [16] V. Rahmani, N. Pelechano, Multi-agent parallel hierarchical path finding in navigation meshes (MA-HNA∗), Computers & Graphics , 2019.
[17] Noumsi, A., Derrien, S. and Quinton, P., “Acceleration of a content-based image-retrieval application on the RDISK cluster”, In Proceedings 20th IEEE International Parallel & Distributed Processing Symposium, IEEE, (2006), 1–10.
[18] Wasson, V., “An efficient content based image retrieval based on speeded up robust features (SURF) with optimization technique”, In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE, (2017), 730–735.
[19] Markowska-Kaczmar, U., & Kwaśnicka, H. (2018). Deep learning—a new era in bridging the semantic gap. Intelligent Systems Reference Library, 145, 123–159..
[20] He, R., Zhu, Y. and Zhan, W., “Fast Manifold-Ranking for contentbased image retrieval”, In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (Vol. 2), IEEE, (2009), 299–302.
[21] Tanioka, H., “A Fast Content-Based Image Retrieval Method Using Deep Visual Features”, In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) (Vol. 5), IEEE, (2019), 20–23.
[22] Zargari, F., Mosleh, A. and Ghanbari, M., “A fast and efficient compressed domain JPEG2000 image retrieval method”, IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, (2008), 1886–1893.
[23] Park, M., Jin, J.S. and Wilson, L. S., “Fast content-based image retrieval using quasi-gabor filter and reduction of image feature dimension”, In Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, IEEE, (2002), 178–182.
[24] Schaefer, G., “Fast Compressed Domain JPEG Image Retrieval”, In 2017 International Conference on Vision, Image and Signal Processing (ICVISP), IEEE, (2017), 22–26. [25] Yang, J., Jiang, B., Li, B., Tian, K. and Lv, Z., “A fast image retrieval method designed for network big data”, IEEE Transactions on Industrial Informatics, Vol. 13, No. 5, (2017), 2350–2359. [26] Sreedevi, S. and Sebastian, S., “Fast image retrieval with feature levels”, In 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy, IEEE, (2013), 1–4.
[27] Mehrabi, M., Zargari, F., Ghanbari, M. and Shayegan, M. A., “Fast content access and retrieval of JPEG compressed images”, Signal Processing: Image Communication, Vol. 46, (2016), 54– 59.
[28] Anwar, S.M., Arshad, F. and Majid, M., “Fast wavelet based image characterization for content based medical image retrieval”, In 2017 International Conference on communication, computing and digital systems (C-CODE), IEEE, (2017), 351– 356. [29] Devi, S. and Mathew, A., “Fast image retrieval using Error Diffusion Block Truncation Coding and unsupervised clustering”, In 2016 International Conference on Emerging Technological Trends (ICETT), IEEE, (2016), 1–6.
[30] Ksantini, R., Ziou, D., Colin, B. and Dubeau, F., “Logistic Regression Models for a Fast CBIR Method Based on Feature Selection”, In Proceedings of the 20th international joint conference on Artifical intelligence, (2007), 2790–2795. [31] Kakde, B. and Okade, M., “A Novel Technique for Fast ContentBased Image Retrieval sing Dual-Cross Patterns”, In 2018 3rd International Conference for Convergence in Technology (I2CT), IEEE, (2018), 1–5.
[32] J.C. Russ, “The image processing handbook”, CRC Press, 1999.
[33] G. Sharma, “Digital color imaging handbook”, CRC Press, 2003.
[34] J. Chamorro-Martnez, J.M. Medina, C. Barranco, E. Galn-Perales, and J.M. Soto-Hidalgo, “An approach to image retrieval on fuzzy objectrelational database using dominant color descriptors”, in Proceedings of the 4th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT, 2005, pp. 676–684.
[35] R. Fuller, “On product-sum of triangular fuzzy numbers”, Fuzzy Sets and Systems, vol. 41(1), pp. 83–87, 1991.
[36] D. Van der Weken, M. Nachtegael, E. Kerre, "Using similarity measures for histogram comparison," , International Fuzzy Systems Association World Congress, pp. 396-403, 2003.
[37] G. Weiss, Multiagent Systems—A Modern Approach to Distributed Artificial intelligence, MIT Press, Cambridge, MA, 1999.
[38] M.A. Hale, J. Craig, Preliminary development of agent technologies for a design integration framework, in: Proceedings of the Fifth Symposium on Multidisciplinary Analysis and Optimization, Panama City, FL, 1994.
[39] N. Jennings and M. Wooldridge, “Intelligent Agents: Theory and Practice,” The Knowledge Eng. Rev., vol. 10, no. 2, 1995, pp. 115–152.
[40] G. Weiss, Multiagent Systems—A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
[41] Guccione, S.A. “Reconfigurable computing at Xilinx “,Proceedings. Euromicro Symposium on Digital Systems Design, Page(s): 102 , 2001.
[42] Becker, J.; Pionteck, T.; Glesner, M. ,“Adaptive systems-on-chip: architectures, technologies and applications” ,14th Symposium on Ingegrated Circuits and Systems Design, 2001.
[43] Hartenstein, R. ,“Coarse grain reconfigurable architectures “,Design Automation Conference, 2001. Proceedings of the ASP DAC 2001. Page(s): 564 –569, Asia and SouthPacific,2001.
[44] Hamid R. Naji ,Letha Etzkorn, Reza Adhami, B. Earl Wells, "Parallel Image Processing with Agent-based Reconfigurable Hardware," Proceedings of the 15th International Conference on Parallel and Distributed Computing Systems (PDCS 2002), September 2002, Louisville, KY.
[45] Hamid R. Naji ,B. Earl Wells,“On Incorporating Multi Agents in Combined Hardware /Software based Reconfigurable Systems, A General Architectural Framework,” Proceedings of the 2002 Southeastern Symposium on System Theory, Huntsville, AL , March 2002.
[46] Hamid R. Naji ,B. Earl Wells, M. Aborizka, “Hardware Agents,” Proceedings of the ISCA 11th International Conference on Intelligent Systems on Emerging Technologies(ICIS-2002),Boston, MA, July 2002.
[48] Jun Yue , Zhenbo Li , Lu Liu , Zetian Fu, "Content-based image retrieval using color and texture fused features", Mathematical and Computer Modelling 54 (2011) 1121–1127.
[49] Mehtre, B.M., M.S. Kankanhalli, A.D. Narasimhalu and G.C. Man,. Color matching for image retrieval, Pattern Recognition Letters 16,( 331-325 , 1995 . | ||
|
آمار تعداد مشاهده مقاله: 302 تعداد دریافت فایل اصل مقاله: 325 |
||