http://iet.metastore.ingenta.com
1887

Colour image retrieval based on the hypergraph combined with a weighted adjacent structure

Colour image retrieval based on the hypergraph combined with a weighted adjacent structure

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

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(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
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Content-based image retrieval (CBIR) is a research hotspot. To improve the performance of a CBIR system, especially the retrieval accuracy, this work proposes a method that uses a soft hypergraph combined with a weighted adjacent structure (WAS) to retrieve images. In this method, the similarities between images are computed and a similarity matrix is constructed by a conjoined colour difference histogram and micro-structure descriptor method. Furthermore, a novel WAS and a soft hypergraph model are utilised to further improve the retrieval precision. The proposed method is compared with other methods in several datasets. Experimental results manifest the performance and robustness of this proposed method.

References

    1. 1)
      • 1. Datta, R., Joshi, D., Li, J., et al: ‘Image retrieval: ideas, influences, and trends of the new age’, ACM Comput. Surv., 2008, 40, (2), pp. 160.
    2. 2)
      • 2. Qi, Y., Zhang, G.: ‘Strategy of active learning support vector machine for image retrieval’, IET Comput. Vis., 2016, 10, (1), pp. 8794.
    3. 3)
      • 3. Vipparthi, S.K., Murala, S., Gonde, A.B., et al: ‘Local directional mask maximum edge patterns for image retrieval and face recognition’, IET Comput. Vis., 2016, 10, (3), pp. 182192.
    4. 4)
      • 4. Akgül, C.B., Rubin, D.L., Napel, S., et al: ‘Content-based image retrieval in radiology: current status and future directions’, J. Digit. Imaging, 2011, 24, (2), pp. 208222.
    5. 5)
      • 5. Mohamadzadeh, S., Farsi, H.: ‘Content-based image retrieval system via sparse representation’, IET Comput. Vis., 2016, 10, (1), pp. 95102.
    6. 6)
      • 6. Tong, S., Chang, E.: ‘Support vector machine active learning for image retrieval’. ACM Int. Conf. on Multimedia, 2001, pp. 107118.
    7. 7)
      • 7. Wang, L., Yang, B., Abraham, A.: ‘Distilling middle-age cement hydration kinetics from observed data using phased hybrid evolution’, Soft Comput., 2015, 20, (9), pp. 120.
    8. 8)
      • 8. Swain, M.J., Ballard, D.H.: ‘Color indexing’, Int. J. Comput. Vis., 1991, 7, (1), pp. 1132.
    9. 9)
      • 9. Pass, G., Zabih, R., Miller, J.: ‘Comparing images using color coherence vectors’. ACM Int. Conf. on Multimedia, 1997, pp. 6573.
    10. 10)
      • 10. Huang, J., Kumar, S.R., Mitra, M., et al: ‘Image indexing using color correlograms’. Conf. on Computer Vision and Pattern Recognition, 1997, p. 762.
    11. 11)
      • 11. Salembier, P., Sikora, T., Manjunath, B.S.: ‘Introduction to MPEG-7: multimedia content description interface’ (John Wiley & Sons Press, New York, 2002, 1st edn.).
    12. 12)
      • 12. Cross, G.R., Jain, A.K.: ‘Markov random field texture models’, IEEE Trans. Pattern Anal. Mach. Intell., 1983, 5, (1), pp. 2539.
    13. 13)
      • 13. Quellec, G., Lamard, M., Cazuguel, G., et al: ‘Fast wavelet-based image characterization for highly adaptive image retrieval’, IEEE Trans. Image Process., 2012, 21, (4), p. 1613.
    14. 14)
      • 14. Kim, W.Y., Kim, Y.S.: ‘A region-based shape descriptor using Zernike moments’, Signal Process. Image Commun., 2000, 16, (1), pp. 95102.
    15. 15)
      • 15. Amanatiadis, A., Kaburlasos, V.G., Gasteratos, A., et al: ‘Evaluation of shape descriptors for shape-based image retrieval’, IET Image Process., 2011, 5, (5), pp. 493499.
    16. 16)
      • 16. Bay, H., Tuytelaars, T., Gool, L.V.: ‘SURF: speeded up robust features’, Comput. Vis. Image Underst., 2006, 110, (3), pp. 404417.
    17. 17)
      • 17. Liu, G.H., Yang, J.Y.: ‘Content-based image retrieval using color difference histogram’, Pattern Recognit., 2013, 46, (1), pp. 188198.
    18. 18)
      • 18. Liu, G.H., Li, Z.Y., Zhang, L., et al: ‘Image retrieval based on micro-structure descriptor’, Pattern Recognit., 2011, 44, (9), pp. 21232133.
    19. 19)
      • 19. He, J., Li, M., Zhang, H.J., et al: ‘Generalized manifold-ranking-based image retrieval’, IEEE Trans. Image Process., 2006, 15, (10), pp. 31703177.
    20. 20)
      • 20. Huang, Y., Liu, Q., Zhang, S., et al: ‘Image retrieval via probabilistic hypergraph ranking’. Computer Vis. Pattern Recognit., 2010, pp. 33763383.
    21. 21)
      • 21. Liu, Q., Huang, Y., Metaxas, D.N.: ‘Hypergraph with sampling for image retrieval’, Pattern Recognit., 2011, 44, (10–11), pp. 22552262.
    22. 22)
      • 22. Gao, Y., Wang, M., Tao, D., et al: ‘3-D object retrieval and recognition with hypergraph analysis’, IEEE Trans. Image Process., 2012, 21, (9), pp. 42904303.
    23. 23)
      • 23. Gao, Y., Dai, Q.H.: ‘Efficient view-based 3-d object retrieval via hypergraph learning’, Tsinghua Sci. Technol., 2014, 19, (3), pp. 250256.
    24. 24)
      • 24. Ciocca, G., Schettini, R.: ‘Using a relevance feedback mechanism to improve content-based image retrieval’. Int. Conf. on Visual Information and Information Systems, 1999, pp. 107114.
    25. 25)
      • 25. Zhang, L., Wang, L., Lin, W.: ‘Semisupervised biased maximum margin analysis for interactive image retrieval’, IEEE Trans. Image Process., 2012, 21, (4), pp. 22942308.
    26. 26)
      • 26. Zhang, L., Wang, L., Lin, W., et al: ‘Geometric optimum experimental design for collaborative image retrieval’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (2), pp. 346359.
    27. 27)
      • 27. Zhang, L., Wang, L., Lin, W.: ‘Conjunctive patches subspace learning with side information for collaborative image retrieval’, IEEE Trans. Image Process., 2012, 21, (8), p. 3707.
    28. 28)
      • 28. Liu, G.H., Yang, J.Y., Li, Z.Y.: ‘Content-based image retrieval using computational visual attention model’, Pattern Recognit., 2015, 48, (8), pp. 25542566.
    29. 29)
      • 29. Scholkopf, B., Platt, J., Hofmann, T.: ‘Learning with hypergraphs: clustering, classification, and embedding’. Int. Conf. on Neural Information Processing Systems, 2006, pp. 16011608.
    30. 30)
      • 30. Hu, R., Collomosse, J.: ‘A performance evaluation of gradient field HOG descriptor for sketch based image retrieval’, Comput. Vis. Image Underst., 2013, 117, (7), pp. 790806.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0566
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0566
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address