Locality-sensitive hashing for region-based large-scale image indexing

Locality-sensitive hashing for region-based large-scale image indexing

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors present an efficient method for approximate large-scale image indexing and retrieval. The proposed method is mainly based on the visual content of the image regions. Indeed, regions are obtained by a fuzzy segmentation and they are described using high-frequency sub-band wavelets. Moreover, because of the difficulty in managing a huge amount of data, which is caused by the exponential growth of the processing time, approximate nearest neighbour algorithms are used to improve the retrieval speed. Therefore they adopted locality-sensitive hashing (LSH) for region-based indexing of images. In particular, since LSH performance depends fundamentally on the hash function partitioning the space, they exposed a new function, inspired from the E 8 lattice, that can efficiently be combined with the multi-probe LSH and the query-adaptive LSH . To justify the adopted theoretical choices and to highlight the efficiency of the proposed method, a set of experiments related to the region-based image retrieval are carried out on the challenging ‘Wang’ data set.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 5. Lazaridis, M., Axenopoulos, A., Rafailidis, D., Daras, P.: ‘Multimedia search and retrieval using multimodal annotation propagation and indexing techniques’, Signal Process.: Image Commun., 2013, 28, (4), pp. 351367.
    6. 6)
    7. 7)
      • 7. Ge, T., He, K., Ke, Q., Sun, J.: ‘Optimized product quantization for approximate nearest neighbor search’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 29462953.
    8. 8)
      • 8. Jégou, H., Amsaleg, L., Schmid, C., Gros, P.: ‘Query-adaptative locality sensitive hashing’. Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 2008, pp. 825828.
    9. 9)
      • 9. Kong, W., Li, W., Guo, M.: ‘Manhattan hashing for large-scale image retrieval’. Proc. of the Int. Conf. on Research and Development in Information Retrieval, 2012, pp. 4554.
    10. 10)
    11. 11)
      • 11. Li, X., Lin, G., Shen, C., van den Hengel, A., Dick, A.: ‘Learning hash functions using column generation’, CoRR, 2013, abs/1303.0339.
    12. 12)
      • 12. Li, P., Wang, M., Cheng, J., Xu, C., Lu, H.: ‘Spectral hashing with semantically consistent graph for image indexing., IEEE Trans.Multimed.’, 2013, 15, (1), pp. 141152.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 19. Hess, S., Piatkowski, N., Morik, K.: ‘Shrimp: descriptive patterns in a tree’. LWA, 2014, pp. 181192.
    20. 20)
    21. 21)
      • 21. Sudhamani, M.V.: ‘Multidimensional indexing structures for content-based image retrieval: a survey’, Int. J. Innov. Comput., Inf. Control, 2008, 4, pp. 867881.
    22. 22)
      • 22. Urruty, T.: ‘Optimisation de l'indexation multidimensionnelle: application aux descripteurs multimédia’. PhD thesis, University of Science and Technology of Lille, 2007.
    23. 23)
      • 23. Philipp-Foliguet, S., Vieira, M.B., Lekkat, M.: ‘Image retrieval by fuzzy regions sets matching’, Inf. Interact. Intell., 2005, 5, pp. 939.
    24. 24)
      • 24. Gallas, A., Barhoumi, W., Zagrouba, E.: ‘Image retrieval by comparison between complete oriented graphs of fuzzy regions’. Proc. of the Int. Conf. on Intelligent Computer Communication and Processing, 2012, pp. 173180.
    25. 25)
      • 25. Gallas, A., Barhoumi, W., Zagrouba, E.: ‘Image retrieval based on wavelet sub-bands and fuzzy weighted regions’. Int. Conf. on Communications and Information Technology (ICCIT), 2012 , 2012, pp. 3337.
    26. 26)
    27. 27)
      • 27. Joly, A., Buisson, O.: ‘A posteriori multi-probe locality sensitive hashing’. Proc. of Int. Conf. on Multimedia, 2008, pp. 209218.
    28. 28)

Related content

This is a required field
Please enter a valid email address