A Semi-local Method for Image Retrieval

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)

   Article  Google Scholar 

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. 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. 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)

   Article  Google Scholar 

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. 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)

   Chapter  Google Scholar 

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. 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. 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. Ro, Y.M., Kim, M., Kang, H.K., Manjunath, B.S.: MPEG-7 homogeneous texture descriptor. ETRI J. 23, 41–51 (2001)

    Article  Google Scholar 

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. David, L.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

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. 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. 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. Wu, C.: SiftGPU: a GPU implementation of scale invariant feature transform (SIFT) (2007)

    Google Scholar 

17. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: ECCV, pp. 404–417 (2006)

    Google Scholar 

18. Uijlings, J.R.R., Smeulders, A.W.M.: Visualising bag-of-words. In: Demo at ICCV (2011)

    Google Scholar 

19. Li, X.: Image retrieval based on perceptive weighted color blocks. Pattern Recogn. Lett. 24, 1935–1941 (2003)

    Article  Google Scholar 

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)

    Chapter  Google Scholar 

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. Yang, J., Li, Y., Tian, Y., Duan, L., Gao, W.: Group-sensitive multiple kernel learning for object categorization. In: ICCV (2009)

    Google Scholar 

23. Harada, T., Ushiku, Y., Yamashita, Y., Kuniyoshi, Y.: Discriminative spatial pyramid. In: IEEE-CVPR, pp. 1617–1624 (2011)

    Google Scholar 

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. 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. Grauman, K., Darrell, T.: The pyramid match kernel: efficient learning with sets of features. J. Mach. Learn. Res. 8, 725–760 (2007)

    MATH  Google Scholar 

27. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Fourth, pp. 147–151 (1988)

    Google Scholar 

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)

    Article  Google Scholar 

Download references