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Image Segmentation and Object Recognition Using Machine Learning

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Smart Trends in Information Technology and Computer Communications (SmartCom 2016)

Abstract

The digital image processing is a fast pace growing field which requires pre-processing of the images before their actual usage and experimentation. Image Segmentation and Object Recognition is one of the phase which requires the prior information about the object to give the extracted attributes as output of the images. Here, in the present research work we have already some outdoor segmented images which have been identified as to which category of classification they belong. This work has been accelerated with the help of machine learning that is a novel technique to identify the objects with outstanding accuracy. The machine learning algorithms have aided in this process of identifying the objects like sky, brick, cement, grass etc. This collaboration of image segmentation with machine learning has proved to be accessible in large datasets where after segmentation images can classify themselves into a category provided it has attributes of the images.

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References

  1. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006). Third Quarter

    Article  Google Scholar 

  2. Bali, A., Shailendra, N.S.: A review on the strategies and techniques of image segmentation. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT). IEEE (2015)

    Google Scholar 

  3. Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309–314 (2004). ACM

    Article  Google Scholar 

  4. Brejl, M., Sonka, M.: Edge-based image segmentation: machine learning from examples. In: International Joint Conference on World Congress on Computational Intelligence on Neural Networks Proceedings, vol. 2. IEEE (1998)

    Google Scholar 

  5. Yan-Li, A.: Introduction to digital image pre-processing and segmentation. In: Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE (2015)

    Google Scholar 

  6. Tang, J.: A color image segmentation algorithm based on region growing. In: 2nd International Conference on Computer Engineering and Technology (ICCET), vol. 6. IEEE (2010)

    Google Scholar 

  7. Ramadevi, Y., Kalyani, B., Sridevi, T.: Synergy between object recognition and image segmentation. Int. J. Comput. Sci. Eng. 2, 2767–2772 (2010)

    Google Scholar 

  8. Rashidi, A., Sigari, M.H., Maghiar, M., Citrin, D.: An analogy between various machine-learning techniques for detecting construction materials in digital images. KSCE J. Civ. Eng. 20, 1178–1188 (2015). Springer

    Article  Google Scholar 

  9. Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Cambridge, Massachusetts London, England (2010)

    MATH  Google Scholar 

  10. Liaw, A., Wiener, M.: Classification and regression by randomForest. R News. 2(3), 18–22 (2002)

    Google Scholar 

  11. Burges, J.C.: A tutorial on support vector machines for pattern recognition. Bell laboratories. Lucent technologies. Data Min. Knowl. Discov. 2, 121–167 (1998)

    Article  Google Scholar 

  12. An, T.K., Kim, M.H.: A new diverse adaboost classifier. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 359–363 (2010)

    Google Scholar 

  13. Wu, X., Kumar, V.: The Top Ten Algorithms in Data Mining. Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC, Minneapolis, Minnesota, USA (2009)

    Book  Google Scholar 

  14. https://goo.gl/mTetx0

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Correspondence to Ashima Sood .

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© 2016 Springer Nature Singapore Pte Ltd.

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Sood, A., Sharma, S. (2016). Image Segmentation and Object Recognition Using Machine Learning. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_25

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  • DOI: https://doi.org/10.1007/978-981-10-3433-6_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3432-9

  • Online ISBN: 978-981-10-3433-6

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