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Stream processing to solve image search by similarity
Keywords:
stream processing, metrics spaces, MPEG-7, sparse spatial selectionAbstract
The classic use of Stream Processing platforms enables working with data in real time, which allows you to generate data analysis quickly attending to a decisionmaking process. However, you can use these platforms for other applications such as indexing and subsequent use of similarity search objects in a database. The images can be displayed on a metric space, which has features that allow rules to discard a not similar image quickly without making costly computations. This paper presents the use of a Stream Processing platform to index images generated by different users. For this, it is necessary to represent these images by vectors containing different MPGE-7 features. This paper shows a Stream Processing platform using its processing elements (PEs) in parallel to speed up the operations involved in the index construction.
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References
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[ZADB06] Zezula P., Amato G., Dohnal V., Batko M. Similarity Search: The Metric Space Approach, Advances in Database Systems, Springer, 2006.
[B13] Barlow, Real-Time Big Data Analytics: Emerging Architecture. Kindle Edition. O'Reilly Media Inc. 2013.
[BFPR06] Brisaboa N.R., Farina A., Pedreira O., Reyes N.: Similarity search using sparse pivots for efficient multimedia information retrieval. In: ISM. pp. 881–888 (2006).
[BNC03] Bustos B., Navarro G. and Chávez E. Pivot selection techniques for proximity searching in metric spaces, Pattern Recognition Letters, Volume 24, Issue 14, October 2003, Pages 2357-2366, ISSN 0167-8655.
[CNBYM01] Chávez E., Navarro G. and Baeza-Yates R., and Marroquín J L. Searching in metric spaces. ACM Comput. Surv. 33, 3 (September 2001), 273-321.
[Food] The Food-101 Data Set: http://www.vision.ee.ethz.ch/datasets extra/food-101/
[DGG15] A. De Mauro, M. Greco, and M. Grimaldi. “What is big data? A consensual definition and a review of key research topics”, in AIP, 2015 pp. 97–104.
[DG04] Dean J. and Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters. COMMUNICATIONS OF THE ACM, 2008, vol. 51, no 1, p. 107.
[HKJR10] Hunt P., Konar M., Junqueira F.P. and Reed B. Zookeeper: wait-free coordination for internet-scale systems. In Proceedings of the 2010 USENIX conference on USENIX annual technical conference, USENIXATC’10, pp 11–11, Berkeley, CA, USA, 2010. USENIX Association.
[L01] D. Laney, “3D Data Management: Controlling Data Volume, Velocity and Variety”, Gartner, 2001, pp. 1-4
[L13] Lux M. LIRE: Open Source Image Retrieval in Java, In Proceedings of the 21st ACM International Conference on Multimedia. New York, NY, USA , pp. 843-846 (2013). ACM.
[MSCJ13] Mayer-Schönberger V., Cukier K., and Jurado A.I.. Big data: La revolución de los datos masivos. Turner. 2013.
[NRNK10] Neumeyer L., Robbins B., Nair A. and Kesari A. ”S4: Distributed Stream Computing Platform,” Data Mining Workshops (ICDMW), 2010 IEEE International Conference on , vol., no., pp.170,177, 13-13 Dec. 2010.
[S414] S4- Distributed Stream Computing Platform: revisada en Julio 2014. http://incubator.apache.org/s4/
[S05] Samet H. Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling). 2005. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
[SBMCA05] Spyrou E., Borgne H. L., Mailis T.P., Cooke E., Avrithis Y.S., and O’Connor N.E. Fusing mpeg-7 visual descriptors for image classification, in ICANN (2), 2005, pp. 847–852.
[ZADB06] Zezula P., Amato G., Dohnal V., Batko M. Similarity Search: The Metric Space Approach, Advances in Database Systems, Springer, 2006.
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Published
2015-11-01
How to Cite
Lobos, J., Gil Costa, G. V., Reyes, N. S., Printista, A. M., & Marín, M. (2015). Stream processing to solve image search by similarity. Journal of Computer Science and Technology, 15(02), p. 93–99. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/548
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Original Articles
