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A Semi-local Method for Image Retrieval

  • Hanen KaramtiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

The visual content of an image is expressed by global or local features. Global features describe some properties of the image such as color, texture and shape. Local features were successfully used for object category recognition and classification to extract the local information from a set of interest points or regions. In this paper, we propose a semi-local method to extract the features based on the previous features extraction methods. Our technique is called the “Spatial Pyramid Matching: SPM”. It works by partitioning the image into increasingly fine sub-regions (or blocs) and computing histograms of global features found inside each bloc.

The results obtained by the proposed method are illustrated through some experiments on Wang and Holidays Dataset. The obtained Results show the simplicity and efficiency of our proposal.

Keywords

Global descriptors Local descriptors Spatial Pyramid Matching Features Image retrieval Visual content Semi-local 

References

  1. 1.
    Flickner, M., Sawhney, H.S., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: the QBIC system. IEEE Comput. 28, 23–32 (1995)CrossRefGoogle Scholar
  2. 2.
    Gony, J., Cord, M., Philipp-Foliguet, S., Gosselin, P.H., Precioso, F., Jordan, M.: RETIN: a smart interactive digital media retrieval system. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 93–96 (2007)Google Scholar
  3. 3.
    Hsu, W., Long, L.R., Antani, S.K.: SPIRS: a framework for content-based image retrieval from large biomedical databases. In: (MEDINFO) - Proceedings of the 12th World Congress on Health (Medical) Informatics - Building Sustainable Health Systems, pp. 188–192 (2007)Google Scholar
  4. 4.
    Deserno, T.M., Guld, M.O., Plodowski, B., Spitzer, K., Wein, B.B., Schubert, H., Ney, H., Seidl, T.: Extended query refinement for medical image retrieval. J. Digit. Imaging 21, 280–289 (2008)CrossRefGoogle Scholar
  5. 5.
    Schettini, R., Ciocca, G., Gagliardi, I.: Feature extraction for content-based image retrieval. In: Encyclopedia of Database Systems, pp. 1115–1119 (2009)Google Scholar
  6. 6.
    Anh, N.D., Bao, P.T., Nam, B.N., Hoang, N.H.: A new CBIR system using SIFT combined with neural network and graph-based segmentation. In: Intelligent Information and Database Systems, Second International Conference, ACIIDS, pp. 294–301 (2010)Google Scholar
  7. 7.
    Fung, Y.-H., Chan, Y.-H.: Producing color-indexed images with scalable color and spatial resolutions. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Hong Kong, 16–19 December, pp. 8–13 (2015)Google Scholar
  8. 8.
    Imran, M., Hashim, R., Khalid, N.E.A.: Segmentation-based fractal texture analysis and color layout descriptor for content based image retrieval. In: 14th International Conference on Intelligent Systems Design and Applications, ISDA, pp. 30–33 (2014)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)Google Scholar
  10. 10.
    Ro, Y.M., Kim, M., Kang, H.K., Manjunath, B.S.: MPEG-7 homogeneous texture descriptor. ETRI J. 23, 41–51 (2001)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, vol. 2, p. 1150 (1999)Google Scholar
  12. 12.
    David, L.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Roy, S.K., Bhattacharya, N., Chanda, B., Chaudhuri, B.B., Ghosh, D.K.: FWLBP: a scale invariant descriptor for texture classification (2018)Google Scholar
  14. 14.
    Douze, M., Jegou, H., Sandhawalia, H., Amsaleg, L., Schmid, C.: Evaluation of GIST descriptors for web-scale image search. In: Proceedings of the 8th ACM International Conference on Image and Video Retrieval, CIVR, Santorini Island, Greece, 8–10 July, p. 19 (2009)Google Scholar
  15. 15.
    Delaitre, V., Laptev, I., Sivic, J.: Recognizing human actions in still images: a study of bag-of-features and part-based representations. In: Proceedings of the British Machine Vision Conference, pp. 1–11 (2010)Google Scholar
  16. 16.
    Wu, C.: SiftGPU: a GPU implementation of scale invariant feature transform (SIFT) (2007)Google Scholar
  17. 17.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: ECCV, pp. 404–417 (2006)Google Scholar
  18. 18.
    Uijlings, J.R.R., Smeulders, A.W.M.: Visualising bag-of-words. In: Demo at ICCV (2011)Google Scholar
  19. 19.
    Li, X.: Image retrieval based on perceptive weighted color blocks. Pattern Recogn. Lett. 24, 1935–1941 (2003)CrossRefGoogle Scholar
  20. 20.
    Takala, V., Ahonen, T., Pietikäinen, M.: Block-based methods for image retrieval using local binary patterns. In: Proceedings of the 14th Scandinavian Conference on Image Analysis (SCIA), pp. 882–891 (2005)Google Scholar
  21. 21.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
  22. 22.
    Yang, J., Li, Y., Tian, Y., Duan, L., Gao, W.: Group-sensitive multiple kernel learning for object categorization. In: ICCV (2009)Google Scholar
  23. 23.
    Harada, T., Ushiku, Y., Yamashita, Y., Kuniyoshi, Y.: Discriminative spatial pyramid. In: IEEE-CVPR, pp. 1617–1624 (2011)Google Scholar
  24. 24.
    Doretto, G., Yao, Y.: Region moments: fast invariant descriptors for detecting small image structures. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3019–3026 (2010)Google Scholar
  25. 25.
    Hur, J., Lim, H., Park, C., Chul Ahn, S.: Generalized deformable spatial pyramid: geometry-preserving dense correspondence estimation. In: IEEE-CVPR, pp. 1392–1400 (2015)Google Scholar
  26. 26.
    Grauman, K., Darrell, T.: The pyramid match kernel: efficient learning with sets of features. J. Mach. Learn. Res. 8, 725–760 (2007)zbMATHGoogle Scholar
  27. 27.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Fourth, pp. 147–151 (1988)Google Scholar
  28. 28.
    Karamti, H., Tmar, M., Visani, M., Urruty, T., Gargouri, F.: Vector space model adaptation and pseudo relevance feedback for content-based image retrieval. Multimed. Tools Appl. 77, 5475–5501 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.MIRACL-ISIMSSfaxTunisia
  2. 2.Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

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