Implementation of Image Mining technique based Hadoop Map Reduce
Keywords:
image mining, Hadoop, mapreduce, image processing, Bigdata
Abstract
Image mining is a very important technique that is used to get information directly from images. In the era of
big data and the fast growth in digital picture complexity Consequently, image mining technology now faces additional
obstacles, such as a fair amount of time cannot be allotted for the analysis of such massive datasets using a personal
computer or database process because there are limits to how much data can be stored and how much it can be computed.
As a result, distributed computing is required for current image exclude collection mining. The Hadoop platform is an
appropriate paradigm; because of its reliance on MapReduce functionalities. This paper gives an overview of the different
image mining techniques that have been suggested in the past some of them are implemented using Hadoop map reduce.
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[2] Ennouni, A., et al. A review on image mining. in 2017 Intelligent Systems and Computer Vision (ISCV). 2017.
IEEE.
[3] Sudhir, R.J.C.E. and I. Systems, A survey on image mining techniques: theory and applications. 2011. 2(6): p.
44-52.
[4] Hsu, W., M.L. Lee, and J.J.J.o.i.i.s. Zhang, Image mining: Trends and developments. 2002. 19(1): p. 7-23.
[5] Min, H. and Y. Shuangyuan. Overview of image mining research. in 2010 5th International Conference on
Computer Science & Education. 2010. IEEE.
[6] De Mauro, A., M. Greco, and M. Grimaldi. What is big data? A consensual definition and a review of key
research topics. in AIP conference proceedings. 2015. American Institute of Physics.
[7] Hsu, W., M.L. Lee, and K.G. Goh. Image mining in IRIS: Integrated retinal information system. in Proceedings
of the 2000 ACM SIGMOD international conference on Management of data. 2000.
[8] Asanobu, K. Data mining for typhoon image collection. in Proceedings of the second international conference
on multimedia data mining. 2001. Citeseer.
[9] Ordonez, C. and E. Omiecinski. Discovering association rules based on image content. in Proceedings IEEE
Forum on Research and Technology Advances in Digital Libraries. 1999. IEEE.
[10] Tang, Y., et al., OEHadoop: accelerate Hadoop applications by co-designing Hadoop with data center network.
2018. 6: p. 25849-25860.
[11] A. Madhuri, S. P. Praveen, D. L. S. Kumar, S. Sindhura, and S. S. J. A. o. t. R. S. f. C. B. Vellela, "Challenges
and Issues of Data Analytics in Emerging Scenarios for Big Data, Cloud and Image Mining," pp. 412-423, 2021.
[12] E. ZAGAN, M. J. I. J. o. A. C. S. DANUBIANU, and Applications, "HADOOP: A Comparative Study between
Single-Node and Multi-Node Cluster," vol. 12, no. 2, 2021.
[13] W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, "Feature extraction methods: a review," in Journal of
Physics: Conference Series, 2020, vol. 1591, no. 1, p. 012028: IOP Publishing.
[14] Foschi, P.G., et al. Feature Extraction for Image Mining. in Multimedia information systems. 2002.
[15] Silakari, S., M. Motwani, and M.J.a.p.a. Maheshwari, Color image clustering using block truncation algorithm.
2009.
[16] Dubey, R.S., et al., Multi feature content based image retrieval. 2010. 2(6): p. 2145-2149.
[17] Biswas, S., T.-h. Kim, and D.J.I.J.o.S.H. Bhattacharyya, Features extraction and verification of signature image
using clustering technique. 2010. 4(3): p. 43-55.
[18] Chang, E.Y., et al. Searching near-replicas of images via clustering. in Multimedia Storage and Archiving
Systems IV. 1999. SPIE.
[19] Chary, R., D.R. Lakshmi, and K.J.a.p.a. Sunitha, Feature extraction methods for color image similarity. 2012.
[20] Al Zudool, M.A., S. Khawatreh, and Z.A. Alqadi, Efficient Methods used to Extract Color Image Features. 2017,
IJCSMC.
[21] Kannan, A., V. Mohan, and N.J.a.p.a. Anbazhagan, An effective method of image retrieval using image mining
techniques. 2010.
[22] Zhang, D., et al., Content-based image retrieval using Gabor texture features. 2000. 3656: p. 13âĂŞ15.
[23] Zhang, J., et al. A parallel k-means clustering algorithm with mpi. in 2011 Fourth International Symposium on
Parallel Architectures, Algorithms and Programming. 2011. IEEE.
[24] Z. A. M. . Shamki and F. . Rabee, “Image mining technique using Hadoop map reduce over distributed multinode computers connections”, AJEST, vol. 1, no. 2, pp. 18–24, Jul. 2022.
[25] Lv, Z., et al. Parallel k-means clustering of remote sensing images based on mapreduce. in International
Conference on Web Information Systems and Mining. 2010. Springer.
[26] Yang, M., H. Mei, and D.J.I.J.I.C.I.C. Huang, An effective detection of satellite image via K-means clustering
on Hadoop system. 2017. 13(3): p. 1037-1046.
[27] Almeer, M.H.J.J.o.E.T.i.C. and I. Sciences, Cloud hadoop map reduce for remote sensing image analysis. 2012.
3(4): p. 637-644.
Published
2022-09-23
How to Cite
Muhammad, Z., & Rabee, F. (2022). Implementation of Image Mining technique based Hadoop Map Reduce. Journal of Software Engineering, 1(1), 36-44. Retrieved from https://sjhse.smiu.edu.pk/index.php/SJHSE/article/view/27
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