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Edge and Texture Feature Extraction Using Canny and Haralick Textures on SPARK Cluster

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Proceedings of the 2nd International Conference on Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 828))

Abstract

Image retrieval is the most significant technology forever. Computer vision is improving its trends and methodologies to perform like a human. System can identify any object without a human help by a simple query. To train the system, we need precise algorithms. Content-based image retrieval (CBIR) is the recent emerging trend in computer vision to retrieve relevant images from huge amount of data. This paper used a distributed and parallel processing paradigm to accelerate the retrieval process. SPARK stream processing environment is used on the top of the Hadoop Distributed File System (HDFS). To extract visual content of the images, edge and texture features are used. The distance between query image and database is measured with Mahalanobis distance metric. The performance of the retrieval system has been compared with other distributed image retrieval systems. The proposed methodology using SPARK environment outperforms the existing systems.

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References

  1. Palle A, Kulkarni RB (2016) Classification of medical MRI brain images based on Hadoop. In: Proceedings of the second international conference on information and communication technology for competitive strategies—ICTCS ’16, 1–4. https://doi.org/10.1145/2905055.2905154

  2. Alham NK, Li M, Liu Y, Hammoud S (2011) A MapReduce-based distributed SVM algorithm for automatic image annotation. Comput Math Appl 62(7):2801–2811. https://doi.org/10.1016/j.camwa.2011.07.046

    Article  MATH  Google Scholar 

  3. Sudheer D, Lakshmi AR (2015) Performance evaluation of Hadoop distributed file system. Pseudo Distrib Mode Fully Distrib Mode (9):81–86

    Google Scholar 

  4. Vesset D, Olofson CW, Nadkarni A, Zaidi A, Mcdonough B, Schubmehl D, … Carnelley P (2015) IDC FutureScape: worldwide big data and analytics 2016 predictions. IDC, 1–13

    Google Scholar 

  5. Yamamoto M (2012) Parallel image database processing with Mapreduce and performance evaluation in pseudo distributed mode. Int J Electron Commer Stud 3(2):211–228. https://doi.org/10.7903/ijecs.1092

    Article  Google Scholar 

  6. Raju USN, George S, Praneeth VS, Deo R, Jain P (2015) Content based image retrieval on Hadoop framework. IEEE Int Congr Big Data 2015:661–664. https://doi.org/10.1109/BigDataCongress.2015.103

    Article  Google Scholar 

  7. Hsieh LC, Wu GL, Hsu YM, Hsu W (2014) Online image search result grouping with MapReduce-based image clustering and graph construction for large-scale photos. J Vis Commun Image Represent 25(2):384–395. https://doi.org/10.1016/j.jvcir.2013.12.010

    Article  Google Scholar 

  8. Costantini L, Nicolussi R (2015) Performances evaluation of a novel Hadoop and spark based system of image retrieval for huge collections. Adv Multimedia. https://doi.org/10.1155/2015/629783

    Article  Google Scholar 

  9. Gu C, Gao Y (2012) A content-based image retrieval system based on Hadoop and Lucene. In: 2012 second international conference on cloud and green computing, pp 684–687. https://doi.org/10.1109/CGC.2012.33

  10. Wang X-Y, Yu Y-J, Yang H-Y (2011) An effective image retrieval scheme using color, texture and shape features. Comput Stand Interfaces 33(1):59–68. https://doi.org/10.1016/j.csi.2010.03.004, Arabi PM, Joshi G, Vamsha Deepa N (2016) Performance evaluation of GLCM and pixel intensity matrix for skin texture analysis. Perspect Sci 8:203–206. https://doi.org/10.1016/j.pisc.2016.03.018

  11. Yin D, Liu D (2013) Content-based image retrial based on Hadoop. Math Prob Eng. https://doi.org/10.1155/2013/684615

    Google Scholar 

  12. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Netw Appl 19(2):171–209. https://doi.org/10.1007/s11036-013-0489-0

    Article  Google Scholar 

  13. Moise D, Shestakov D, Gudmundsson G, Amsaleg L (2013) Indexing and searching 100 M images with map-reduce. ICMR 2013:17–24. https://doi.org/10.1145/2461466.2461470

    Article  Google Scholar 

  14. Sakr NA, ELdesouky AI, Arafat H (2016) An efficient fast-response content-based image retrieval framework for big data. Comput Electr Eng 54:522–538. https://doi.org/10.1016/j.compeleceng.2016.04.015

    Article  Google Scholar 

  15. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Recent advances and emerging challenges of feature selection in the context of big data. Knowl-Based Syst 86(May):33–45. https://doi.org/10.1016/j.knosys.2015.05.014

    Article  Google Scholar 

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Authors and Affiliations

  1. SCOPE, VIT University, Vellore, Tamil Nadu, India

    D. Sudheer, R. SethuMadhavi & P. Balakrishnan

Authors
  1. D. Sudheer
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  2. R. SethuMadhavi
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  3. P. Balakrishnan
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Corresponding author

Correspondence to D. Sudheer .

Editor information

Editors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Tai Kang

  4. Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Husseinzadeh Kashan

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Sudheer, D., SethuMadhavi, R., Balakrishnan, P. (2019). Edge and Texture Feature Extraction Using Canny and Haralick Textures on SPARK Cluster. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_56

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  • DOI: https://doi.org/10.1007/978-981-13-1610-4_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1609-8

  • Online ISBN: 978-981-13-1610-4

  • eBook Packages: EngineeringEngineering (R0)

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