A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern

Abstract

In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of bins and performed the voting with saturation values and vice versa by following a principle similar to that of the HOG descriptor, where orientation of the gradient is quantized into a certain number of bins and voting is done with gradient magnitude. This helps us to study the nature of variation of saturation with variation in Hue and nature of variation of Hue with the variation in saturation. The texture component of our descriptor considers the co-occurrence relationship between the pixels symmetric about both the diagonals of a 3 × 3 window. Our work is inspired from the work done by Dubey et al. (IEEE Signal Process Lett 22(9):1215–1219, [2015]). These two components, viz. color and texture information individually perform better than existing texture and color descriptors. Moreover, when concatenated the proposed descriptors provide a significant improvement over existing descriptors for content base color image retrieval. The proposed descriptor has been tested for image retrieval on five databases, including texture image databases—MIT-VisTex database and Salzburg texture database and natural scene databases Corel 1K, Corel 5K and Corel 10K. The precision and recall values experimented on these databases are compared with some state-of-art local patterns. The proposed method provided satisfactory results from the experiments.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Notes

  1. 1.

    http://www.ci.gxnu.edu.cn/cbir/Dataset.aspx.

  2. 2.

    http://www.wavelab.at/sources/STex/.

  3. 3.

    MIT Vision and Modeling Group, Cambridge, Vision texture, available online: http://vismod.media.mit.edu/pub/.

References

  1. 1.

    Dubey SR, Singh SK, Singh RK (2015) Local diagonal extrema pattern: a new and efficient feature descriptor for CT image retrieval. IEEE Signal Process Lett 22(9):1215–1219

    Google Scholar 

  2. 2.

    Haralick RM, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Google Scholar 

  3. 3.

    Zhang J, Li GL, He SW (2008) Texture-based image retrieval by edge detection matching GLCM. In: Proceedings—10th IEEE international conference on high performance computing and communications, HPCC, pp 782–786

  4. 4.

    Partio M, Cramariuc B, Gabbouj M, Visa A (2002) Rock texture retrieval using gray level co-occurrence matrix. In: Proceedings 5th Nord Signal

  5. 5.

    de Siqueira FR, Schwartz WR, Pedrini H (2013) Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120:336–345

    Google Scholar 

  6. 6.

    Li Y, Zhou C, Geng B, Xu C, Liu H (2013) A comprehensive study on learning to rank for content-based image retrieval. Signal Process 93(6):1426–1434

    Google Scholar 

  7. 7.

    Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) Local derivative radial patterns: a new texture descriptor for content-based image retrieval. Signal Process 137:274–286

    Google Scholar 

  8. 8.

    Li W, Pan H, Li P, Xie X, Zhang Z (2017) A medical image retrieval method based on texture block coding tree. Signal Process Image Commun 59:131–139

    Google Scholar 

  9. 9.

    Tiwari AK, Kanhangad V, Pachori RB (2017) Histogram refinement for texture descriptor based image retrieval. Signal Process Image Commun 53:73–85

    Google Scholar 

  10. 10.

    Banerjee P, Bhunia AK, Bhattacharyya A, Roy PP, Murala S (2017) Local neighborhood intensity pattern: a new texture feature descriptor for image retrieval. arXiv Prepr. arXiv:1709.02463

  11. 11.

    Palm C (2004) Color texture classification by integrative co-occurrence matrices. Pattern Recognit 37(5):965–976

    Google Scholar 

  12. 12.

    Jeong S, Won CS, Gray RM (2004) Image retrieval using color histograms generated by Gauss mixture vector quantization. Comput Vis Image Underst 94(1–3):44–66

    Google Scholar 

  13. 13.

    Pass G, Zabih R, Miller J (1998) Comparing images using color coherence vectors. In: Proceedings fourth ACM international conference multimedia (MULTIMEDIA’96), pp 1–14

  14. 14.

    Subrahmanyam M, Wu QMJ, Maheshwari RP, Balasubramanian R (2013) Modified color motif co-occurrence matrix for image indexing and retrieval. Comput Electr Eng 39(3):762–774

    Google Scholar 

  15. 15.

    Baraldi A, Parmiggiani F (1995) An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans Geosci Remote Sens 33(2):293–304

    Google Scholar 

  16. 16.

    Kovalev V, Petrou M (1996) Multidimensional co-occurrence matrices for object recognition and matching. Graph Model Image Process 58(3):187–197

    Google Scholar 

  17. 17.

    Davis LS, Johns SA, Aggarwal JK (1979) Texture analysis using generalized co-occurrence matrices. IEEE Trans Pattern Anal Mach Intell 1(3):251–259

    Google Scholar 

  18. 18.

    Vadivel A, Sural S, Majumdar AK (2007) An integrated color and intensity co-occurrence matrix. Pattern Recognit Lett 28(8):974–983

    Google Scholar 

  19. 19.

    Huang J, Kumar SR, Mitra M, Zhu W-J, Zabih R (1997) Image indexing using color correlograms. In: Proceedings, 1997 IEEE computer society conference on computer vision and pattern recognition, pp 762–768

  20. 20.

    Huang J, Kumar SR, Mitra M (1997) Combining supervised learning with color correlograms for content-based image retrieval. In: Proceedings fifth ACM international conference multimedia—multimedia’97, pp 325–334

  21. 21.

    Park ST, Seo K, Jang D (2005) Expert system based on artificial neural networks for content-based image retrieval. Expert Syst Appl 29(3):589–597

    Google Scholar 

  22. 22.

    Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B (2004) Content based image retrieval using motif cooccurrence matrix. Image Vis Comput 22(14):1211–1220

    Google Scholar 

  23. 23.

    Vipparthi SK, Nagar SK (2014) Multi-joint histogram based modelling for image indexing and retrieval. Comput Electr Eng 40(8):163–173

    Google Scholar 

  24. 24.

    Balmelli L, Mojsilovic A (1999) Wavelet domain features for texture description, classification and replicability analysis. In: Proceedings international conference on image processing, ICIP 99, vol 4, pp 440–444

  25. 25.

    Ardizzoni S, Bartolini I, Patella M (1999) Windsurf: region-based image retrieval using wavelets. In: Proceedings tenth international workshop database expert system application DEXA 99, pp 167–173

  26. 26.

    Wang JZ, Wiederhold G, Firschein O, Wei SX (1997) Content-based image indexing and searching using Daubechies’ wavelets. Int J Digit Libr 1(4):311–328

    Google Scholar 

  27. 27.

    Moghaddam HA, Khajoie TT, Rouhi AH, Tarzjan MS (2005) Wavelet correlogram: a new approach for image indexing and retrieval. Pattern Recognit 38(12):2506–2518

    Google Scholar 

  28. 28.

    Manjunath BS (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Google Scholar 

  29. 29.

    Ahmadian A, Mostafa A (2003) An efficient texture classification algorithm using Gabor wavelet. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society (IEEE Cat. No. 03CH37439), vol 1, pp 930–933

  30. 30.

    Moghaddam HA, Dehaji MN (2013) Enhanced Gabor wavelet correlogram feature for image indexing and retrieval. Pattern Anal Appl 16(2):163–177

    MathSciNet  Google Scholar 

  31. 31.

    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    MATH  Google Scholar 

  32. 32.

    Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    MathSciNet  MATH  Google Scholar 

  33. 33.

    Takala V, Ahonen T, Pietikainen M (2005) Block-based methods for image retrieval using local binary patterns. In: Lecture notes in computer science, vol 3540, pp 882–891

  34. 34.

    Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118

    MathSciNet  MATH  Google Scholar 

  35. 35.

    Heikkilä M, Pietikäinen M, Schmid C (2006) Description of interest regions with center-symmetric local binary patterns. In: Computer vision, graphics and image processing. Springer, Berlin, Heidelberg, pp 58–69

  36. 36.

    He Y, Sang N, Gao C (2012) Multi-structure local binary patterns for texture classification. Pattern Anal Appl 16(4):595–607

    MathSciNet  Google Scholar 

  37. 37.

    Qian X, Hua XS, Chen P, Ke L (2011) PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recognit 44(10–11):2502–2515

    Google Scholar 

  38. 38.

    Tlig L, Sayadi M, Fnaiech F (2012) A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features. Signal Process Image Commun 27(6):694–708

    Google Scholar 

  39. 39.

    Papakostas GA, Koulouriotis DE, Karakasis EG, Tourassis VD (2013) Moment-based local binary patterns: a novel descriptor for invariant pattern recognition applications. Neurocomputing 99:358–371

    Google Scholar 

  40. 40.

    Murala S, Maheshwari RP, Balasubramanian R (2012) Directional local extrema patterns: a new descriptor for content based image retrieval. Int J Multimed Inf Retr 1(3):191–203

    MATH  Google Scholar 

  41. 41.

    Dubey SR, Singh SK, Singh RK (2016) Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J Biomed Heal Inform 20(4):1139–1147

    Google Scholar 

  42. 42.

    Yao CH, Chen SY (2002) Retrieval of translated, rotated and scaled color textures. Pattern Recognit 36(4):913–929

    Google Scholar 

  43. 43.

    Murala S, Wu QMJ (2014) Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J Biomed Heal Inform 18(3):929–938

    Google Scholar 

  44. 44.

    Hamouchene I, Aouat S (2014) A new texture analysis approach for iris recognition. AASRI Proc 9:2–7

    Google Scholar 

  45. 45.

    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    MathSciNet  MATH  Google Scholar 

  46. 46.

    Wu X, Sun J, Fan G, Wang Z (2015) Improved local ternary patterns for automatic target recognition in infrared imagery. Sensors (Switzerland) 15(3):6399–6418

    Google Scholar 

  47. 47.

    Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22(10):4049–4060

    MathSciNet  MATH  Google Scholar 

  48. 48.

    Zhao Y, Jia W, Hu RX, Min H (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:68–76

    Google Scholar 

  49. 49.

    Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886

    MathSciNet  MATH  Google Scholar 

  50. 50.

    Jacob IJ, Srinivasagan KG, Jayapriya K (2014) Local oppugnant color texture pattern for image retrieval system. Pattern Recognit Lett 42(1):72–78

    Google Scholar 

  51. 51.

    Murala S, Wu QMJ (2015) Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval. Neurocomputing 149(PC):1502–1514

    Google Scholar 

  52. 52.

    Bhunia AK, Kishore PSR, Mukherjee P, Das A, Roy PP (2019) Texture synthesis guided deep hashing for texture image retrieval. In: IEEE winter conference on applications of computer vision (WACV), pp 609–618

  53. 53.

    Zhang H, Wang S, Xu X, Chow TWS, Wu QMJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 99:1–15

    MathSciNet  Google Scholar 

  54. 54.

    Wang T et al (2018) Jumping and refined local pattern for texture classification. IEEE Access 6:64416–64426

    Google Scholar 

  55. 55.

    Dong Y, Wu H, Li X, Zhou C, Wu Q (2018) Multiscale symmetric dense micro-block difference for texture classification. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2018.2883825

    Article  Google Scholar 

  56. 56.

    Dong Y, Feng J, Yang C, Wang X, Zheng L, Pu J (2018) Multi-scale counting and difference representation for texture classification. Vis Comput 34(10):1315–1324

    Google Scholar 

  57. 57.

    Dong Y, Feng J, Liang L, Zheng L, Wu Q (2017) Multiscale sampling based texture image classification. IEEE Signal Process Lett 24(5):614–618

    Google Scholar 

  58. 58.

    Dong Y, Tao D, Li X, Ma J, Pu J (2015) Texture classification and retrieval using shearlets and linear regression. IEEE Trans Cybern 45(3):358–369

    Google Scholar 

  59. 59.

    Verma M, Raman B, Murala S (2015) Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 165:255–269

    Google Scholar 

  60. 60.

    Liu G-H, Yang J-Y (2013) Content-based image retrieval using color difference histogram. Pattern Recognit 46(1):188–198

    Google Scholar 

  61. 61.

    Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348

    Google Scholar 

  62. 62.

    Lu Z, Jiang X, Kot A (2017) A novel LBP-based color descriptor for face recognition. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1857–1861

  63. 63.

    Ahmadian A, Mostafa A, Abolhassani M, Salimpour Y (2005) A texture classification method for diffused liver diseases using Gabor wavelets. In: Conference proceedings IEEE engineering in medicine and biology society, vol 2(c), pp 1567–1570

  64. 64.

    Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125

    MATH  Google Scholar 

  65. 65.

    Ning J, Zhang L, Zhang D, Wu C (2009) Robust object tracking using joint color-texture histogram. Int J Pattern Recognit Artif Intell 23(07):1245–1263

    Google Scholar 

  66. 66.

    Moore S, Bowden R (2011) Local binary patterns for multi-view facial expression recognition. Comput Vis Image Underst 115(4):541–558

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ayan Kumar Bhunia.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bhunia, A.K., Bhattacharyya, A., Banerjee, P. et al. A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. Pattern Anal Applic 23, 703–723 (2020). https://doi.org/10.1007/s10044-019-00827-x

Download citation

Keywords

  • Diagonally symmetric co-occurrence pattern
  • Gray level co-occurrence matrix
  • Histogram quantization
  • Corel 1K
  • Corel 5K
  • Corel 10K
  • MIT-VisTex database
  • STex database