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A Survey of Feature Extraction for Content-Based Image Retrieval System

  • Neha GhoshEmail author
  • Shikha Agrawal
  • Mahesh Motwani
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

Content-based image retrieval system (CBIR) is a challenging domain which is used in various fields of research today, such as scientific research, medical, Internet, and other communication media. CBIR is an approach that allows a user to obtain an image depends on a query from large datasets holding a huge amount of images. Images play a big role in any of the media today, where communication and data transmission held using the specific formats of data. Thus, for making communication and information sharing via images, it is needful to perform its extraction and then further processing with information content. A survey has been done on various content-based image retrieval techniques which are derived by the various authors for the feature extraction of images and which are further used for classification.

Keywords

Content-based image retrieval (CBIR) Feature extraction of image Image preprocessing 

References

  1. 1.
    Kashyap, R., Tiwari, V.: Energy-based active contour method for image segmentation. Int. J. Electron. Healthc. 9(2–3), 210–225 (2017)CrossRefGoogle Scholar
  2. 2.
    Smeulders, A.W.M., Santini, S., Worring, M., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  3. 3.
    Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Pektovic, D., Yanker, P., Faloutsos, C., Taubin, G.: The QBIC project: query images using content by color, texture and shape. In: Proceedings of the SPIE Storage and Retrieval for Databases of Image and Video, vol. 1908. SPIE (1993)Google Scholar
  4. 4.
    Smith, J.R., Chang, S.F.: Visual SEEK: fully automated content-based image query system. In: Proceedings of Forth ACM International Conference on Multimedia 96, Boston, MA (1996)Google Scholar
  5. 5.
    Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)CrossRefGoogle Scholar
  6. 6.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application for image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002)CrossRefGoogle Scholar
  7. 7.
    Smith, J.R., Chang, S.F.: Visually searching the web for content. IEEE Multim. 4(3), 12–20 (1997)CrossRefGoogle Scholar
  8. 8.
    Sclaroff, S., LaCascia, M., Sethi, S., Taycher, L.: Unifying textual and visual cues for content-based image retrieval system on the world wide web. Comp. Vis. Image Underst. 75(1–2), 86–98 (1999)Google Scholar
  9. 9.
    Zhou, X.S., Huang, T.S.: CBIR: from low-level features to high level semantics. In: Proceedings of the SPIE, Image and Video Communication and Processing, vol. 3974, pp. 426–431 (2000)Google Scholar
  10. 10.
    Brunelli, R., Mich, O.: Image retrieval by examples. IEEE Trans. Multim. 2(3), 164–171 (2000)CrossRefGoogle Scholar
  11. 11.
    Yue, J., Li, Z., Liu, L.: Content-based image retrieval using color and texture fused features. Math. Comput. Model. 54, 1121–1127 (2011)CrossRefGoogle Scholar
  12. 12.
    Jenni, K., Mandala, S., Sunar, M.S.: CBIR using color string comparison. In: Procedia Comput. Sci. 50, 374–379 (2015)Google Scholar
  13. 13.
    Veltkamp, R.C., Tanase, M.: Content-based image retrieval systems: a survey (2000)Google Scholar
  14. 14.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multim. Comput. Commun. Appl. 2(1), 1–19 (2006)CrossRefGoogle Scholar
  15. 15.
    Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey for content-based image retrieval with high-level semantics. Pattern Recog. 40(1), 262–282 (2007)CrossRefGoogle Scholar
  16. 16.
    Lakshmi A., Rakshit, S.: New curvelet features for image indexing and retrieval. In: Computer Networks and Intelligent Computing, vol. 157, pp. 492–501. Springer-Verlag Berlin Heidelberg (2011)Google Scholar
  17. 17.
    Priyatharshini, R., Chitrakala, S.: Association based image retrieval: a survey. In: Mobile Communication and Power Engineering, vol. 157, pp. 17–26. Springer, Berlin Heidelberg (2013)Google Scholar
  18. 18.
    Li, J., Allinson, N.M.: Relevance feedback in content-based image retrieval: a survey. In: Handbook on Neural Information Processing, vol. 49, pp. 433–469. Springer, Berlin Heidelberg (2013)Google Scholar
  19. 19.
    Ai, L., Yu, J., He, Y., Guan, T.: High-dimensional indexing technologies for large scale content-based image retrieval: a review. J. Zhejiang Univ. Sci. C 14(7), 505–520 (2013)CrossRefGoogle Scholar
  20. 20.
    Manno-Kovacs, A.: Content based image retrieval using salient orientation histograms. In: IEEE International Conference For Image Processing (ICIP), pp. 2480–2484. Phoenix, AZ, USA (2016)Google Scholar
  21. 21.
    Guo, J.-M., Prasetyo, H.: Content-based image retrieval using features extracted from halftoning-based block truncation coding. IEEE Trans. Image Process. 24 (2015)Google Scholar
  22. 22.
    Guo, J.-M., Prasetyo, H., Chen, J.-H.: Content-based image retrieval using error diffusion block truncation coding features. IEEE Trans. Circ. Syst. Video Technol. 25 (2015)Google Scholar
  23. 23.
    Bala, A., Kaur, T.: Local texton XOR patterns: a new feature descriptor for content based image retrieval. Eng. Sci. Technol. Int. J. 19(1), 101–112 (2016)Google Scholar
  24. 24.
    Angelescu, N., Coanda, H.G., Caciula, I., Dragoi, C., Albu, F.: SQL query optimization in content based image retrieval systems. In: Internnational Conference on Communications COMM, pp. 395–398. Bucharest (2016)Google Scholar
  25. 25.
    Mack, P., Megherbi, D.B.: A content-based image retrieval technique with tolerance via multi-page differentiate hashing and binary-tree searching multi-object buckets. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–6. Budapest (2016)Google Scholar
  26. 26.
    Douik, A., Abdellaoui, M., Kabbai, L.: Content based image retrieval using local and global features descriptor. In: 2nd International Conference on Advanced Technology for Signal and Image Processing (ATSIP), pp. 151–154. Monastir (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringUIT-RGPVBhopalIndia

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