The aim of image retrieval systems is to automatically assess, retrieve and represent relative images-based user demand. However, the accuracy and speed of image retrieval are still an interesting topic of many researches. In this study, a new method based on sparse representation and iterative discrete wavelet transform has been proposed. To evaluate the applicability of the proposed feature-based sparse representation for image retrieval technique, the precision at percent recall and average normalised modified retrieval rank are used as quantitative metrics to compare different methods. The experimental results show that the proposed method provides better performance in comparison with other methods.

References

1. 1)
  - 18. Zhang, S., Huang, Q., Hua, G., et al: ‘Building contextual visual vocabulary for large-scale image applications’. ACM Multimedia, 2010, pp. 25–29.
2. 2)
  - 40. Singha, M., Hemachandran, K.: ‘Content based image retrieval using color and texture’, Signal Image Process., Int. J. (SIPIJ), 2012, 3, (1), pp. 39–57 (doi: 10.5121/sipij.2012.3104).
3. 3)
  - 6. Montagna, R., Finlayson, G.D.: ‘Padua point interpolation and Lp-Norm minimization in color-based image indexing and retrieval’, IET Image Process., 2012, 6, (2), pp. 139–147 (doi: 10.1049/iet-ipr.2010.0498).
4. 4)
  - 33. Mohimani, G.H., Babaie-zadeh, M., Jutten, Ch.: ‘A fast approach for overcomplete sparse decomposition based on smoothed ℓ0 norm’, IEEE Trans. Signal Process, 2009, 57, (1), pp. 289–301 (doi: 10.1109/TSP.2008.2007606).
5. 5)
  - 9. Nixon, M., Aguado, A.: ‘Feature extraction & image processing’ (Elsevier Ltd., 2002, 2nd edn., 2008).
6. 6)
  - 35. Flower dataset, Available at: http://www.robots.ox.ac.uk/~vgg/data/flowers/17/.
7. 7)
  - 16. Lu, Y., Tan, C.L.: ‘Information retrieval in document image databases’, IEEE Trans. Knowl. Data Eng., 2004, 16, (11), pp. 1398–1410 (doi: 10.1109/TKDE.2004.76).
8. 8)
  - 42. Hiremath, P.S., Shivashankar, S., Pujari, J.: ‘Wavelet based features for color texture classification with application to CBIR’, IJCSNS Int. J. Comput. Sci. Netw. Secur., 2006, 6, (9), pp. 124–133.
9. 9)
  - 49. Candes, E.J., Romberg, J., Tao, T.: ‘Stable signal recovery from incomplete and inaccurate measurements’, Commun. Pure Appl. Math., 2006, 59, (8), pp. 1207–1223 (doi: 10.1002/cpa.20124).
10. 10)
  - 23. Farsi, H., Mohamadzadeh, S.: ‘Colour and texture feature-based image retrieval by using Hadamard matrix in discrete wavelet transform’, IET Image Process., 2013, 7, (3), pp. 212–218 (doi: 10.1049/iet-ipr.2012.0203).
11. 11)
  - 1. Liu, Y., Zhang, D., Lu, G., Ma, W.: ‘A survey of content-based image retrieval with high-level semantics’, Pattern Recognit., 2007, 40, pp. 262–282 (doi: 10.1016/j.patcog.2006.04.045).
12. 12)
  - 3. Starostenko, O., Chávez-Aragón, A., Burlak, G., et al: ‘A novel star field approach for shape indexing in CBIR system’, J. Eng. Lett., 2007, 1, (2), pp. 10–21.
13. 13)
  - 28. Donoho, D., Tsaig, Y.: ‘Fast solution of l1-norm minimization problems when the solution may be sparse’, preprint, 2006, pp. 4789–4812, http://www.stanford.edu/~tsaig/research.html.
14. 14)
  - 25. Mohamadzadeh, S., Farsi, H.: ‘Image retrieval using color-texture features extracted from Gabor-Walsh wavelet pyramid’, J. Inf. Syst. Telecommun., 2014, 2, (1), pp. 31–40.
15. 15)
  - 21. Srinivas, M., RamuNaidu, R., Sastry, C.S., et al: ‘Content based medical image retrieval using dictionary learning’, Neurocomputing, 2015, 168, (30), pp. 880–895 (doi: 10.1016/j.neucom.2015.05.036).
16. 16)
  - 5. Li, F., Dai, Q., Xu, W., et al: ‘Multi-label neighborhood propagation for region-based image retrieval’, IEEE Trans. Multimedia, 2008, 10, (8), pp. 1592–1604 (doi: 10.1109/TMM.2008.2004914).
17. 17)
  - 26. Mallat, S.A.: ‘Wavelet tour of signal processing’ (Academic press, San Diego, CA, 1999).
18. 18)
  - 34. Veganzones, M.A., Graña, M.: ‘A spectral/spatial CBIR system for hyper spectral images’, IEEE J. Sel. Top. Earth Obs. Remote Sens., 2012, 5, pp. 488–500 (doi: 10.1109/JSTARS.2012.2186629).
19. 19)
  - 39. International organization for standardization, MPEG-7 overview 2004. Available at http://www.mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm, accessed 15 November 2011.
20. 20)
  - 22. Liu, R., Chen, Y., Zhu, X., et al: ‘Image classification using label constrained sparse coding’, Multimedia Tools Appl., 2015, 74, pp. 1–15 (doi: 10.1007/s11042-013-1647-x).
21. 21)
  - 29. Chen, S., Donoho, D., Saunders, M.: ‘Atomic decomposition by basis pursuit’, SIAM Rev., 2001, 43, (1), pp. 129–159 (doi: 10.1137/S003614450037906X).
22. 22)
  - 45. Jiang, Q., We, W., Zhang, H.: ‘New researches about dominant color descriptor and graph edit distance’, Int. Conf. Intell. Human-Mach. Syst. Cybern. (IHMSC), 2011, 1, pp. 50–52.
23. 23)
  - 17. Siddiquie, B., Feris, R., Davis, L.: ‘Image ranking and retrieval based on multi-attribute queries’. CVPR, 2011, pp. 801–808.
24. 24)
  - 27. Amaldi, E., Kann, V.: ‘On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems’, Theor. Comput. Sci., 1998, 209, pp. 237–260 (doi: 10.1016/S0304-3975(97)00115-1).
25. 25)
  - 37. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: ‘The Amsterdam library of object images’, Int. J. Comput. Vis., 2005, 61, pp. 103–112 (doi: 10.1023/B:VISI.0000042993.50813.60).
26. 26)
  - 38. VisTex Texture Dataset. Available at: http://www.vismod.media.mit.edu/vismod/imagery/VisionTexture/.
27. 27)
  - 1. Feng, J., Mingjing, L., Zhang, H.J., et al: ‘A unified framework for image retrieval using keyword and visual features’, IEEE Trans. Image Process., 2005, 14, (7), pp. 979–989 (doi: 10.1109/TIP.2005.847289).
28. 28)
  - 31. Yang, A.Y., Zhou, Z., Ganesh, A., et al: ‘Fast l1-minimization algorithms for robust face recognition’, IEEE Trans. Image Processing, 2013, 22, (8), pp. 3234–3246.
29. 29)
  - 7. Liapis, S., Tziritas, G.: ‘Color and texture image retrieval using chromaticity histograms and wavelet frames’, IEEE Trans. Multimedia, 2004, 6, (5), pp. 676–686.
30. 30)
  - 36. Coral dataset, last referred on June 2009, Available at: http://www.wang.ist.psu.edu/docs/related/.
31. 31)
  - 41. Minh, N.D., Vetterli, M.: ‘Wavelet-based texture retrieval using generalized Gaussian density and Kullback–Leibler distance’, IEEE Trans. Image Process., 2002, 11, (2), pp. 146–158 (doi: 10.1109/83.982822).
32. 32)
  - 44. Ibrahim, A., Zou'bi, A., Sahawneh, R., et al: ‘Fixed representative colours feature extraction algorithm for moving picture experts group-7 dominant color descriptor’, Int. J. Comput. Sci., 2009, 5, (11), pp. 773–777.
33. 33)
  - 4. Marakakis, A., Siolas, G., Galatsanos, N., et al: ‘Relevance feedback approach for image retrieval combining support vector machines and adapted Gaussian mixture models’, IET Image Process., 2011, 5, (6), pp. 531–574 (doi: 10.1049/iet-ipr.2009.0402).
34. 34)
  - 43. Troncy, R., Huet, B., Schenk, S.: ‘Feature extraction for multimedia analysis: ‘Multimedia Semantics, Desktop Edition (XML): Metadata, Analysis and Interaction’ (John Wiley & Sons Inc., New York, 2011, 1st edn.), pp. 36–54.
35. 35)
  - 4. Candes, E., Romberg, J., Tao, T.: ‘Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information’, IEEE Trans. Inf. Theory, 2006, 52, pp. 489–509 (doi: 10.1109/TIT.2005.862083).
36. 36)
  - 46. Gemmeke, J.F., Van Hamme, H., Cranen, B., et al: ‘Compressive sensing for missing data imputation in noise robust speech recognition’, IEEE J. Sel. Top. Signal Process., 2010, 4, (2), pp. 272–287 (doi: 10.1109/JSTSP.2009.2039171).
37. 37)
  - 13. Donoho, D.L.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, pp. 1289–1306 (doi: 10.1109/TIT.2006.871582).
38. 38)
  - 11. Davenport, M.A., Duarte, M.F., Eldar, Y.C., et al: ‘Introduction to compressed sensing’ (Department of Statistics at Stanford University, 2011), pp. 1–68.
39. 39)
  - 33. Chun, Y.D., Kim, N.C., Jang, I.H.: ‘Content-based image retrieval using multi-resolution color and texture features’, IEEE Trans. Multimedia, 2008, 10, (6), pp. 1073–1084 (doi: 10.1109/TMM.2008.2001357).
40. 40)
  - 10. 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), pp. 1613–1623 (doi: 10.1109/TIP.2011.2180915).
41. 41)
  - 19. Kang, L.W., Hsu, C.Y., Chen, H.W., et al: ‘Feature-based sparse representation for image similarity assessment’, IEEE Trans. Multimedia, 2011, 13, (5), pp. 1019–1030 (doi: 10.1109/TMM.2011.2159197).
42. 42)
  - 6. Lin, C.H., Liu, C.W., Chen, H.Y.: ‘Image retrieval and classification using adaptive local binary patterns based on texture features’, IET Image Process., 2012, 6, (7), pp. 822–830 (doi: 10.1049/iet-ipr.2011.0445).
43. 43)
  - 20. Yang, X., Qian, X., Mei, T.: ‘Learning salient visual word for scalable mobile image retrieval’, Pattern Recognit., 2015, 48, (11), pp. 3093–3101 (doi: 10.1016/j.patcog.2014.12.017).
44. 44)
  - 24. Farsi, H., Mohamadzadeh, S.: ‘Combining Hadamard matrix, discrete wavelet transform and DCT features based on PCA and KNN for image retrieval’, Majlesi J. Electr. Eng., 2013, 7, (1), pp. 9–15.
45. 45)
  - 12. Elad, M.: ‘Sparse and redundant representations’ (Springer, New York, 2010).

Content-based image retrieval system via sparse representation

References

Related content