Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1685–1717 | Cite as

Principal texture direction based block level image reordering and use of color edge features for application of object based image retrieval

  • Jitesh PradhanEmail author
  • Arup Kumar Pal
  • Haider Banka
Article

Abstract

In this paper, the authors have presented a novel content-based image retrieval (CBIR) scheme based on the combination of color, shape, and texture visual image features. Initially, the combined features of color and shape are derived from the object region of an image using the proposed color edge map approach. This approach is suitable to extract both the color and shape based features simultaneously from image object region. We have preserved more information associated with the object region and some significant information from the background region for enabling better retrieval efficiency. In the subsequent stage, we have extracted texture features from the preprocessed image. This preprocessed image is obtained after decomposition of an image into non-overlapping blocks followed by reordering all blocks based on their principal texture direction. The notion supports the variation present on image data can be controlled by rearranging each block as per their principal direction and some texture based parameters derived from the preprocessed image. The final feature vector consists of color, shape, and texture-related features in their correct proportions. Proposed CBIR scheme is extensively tested using four coral image databases (i.e. 1,000 color images from 10 different classes, 10,000 color images from 20 different classes, 7,200 images from 100 different classes and 17,125 images from 20 different classes). Experimental results show that the proposed CBIR scheme has better retrieval efficiency in terms of precision and recall than other related schemes.

Keywords

Color edge map Content-based image retrieval (CBIR) Object cropping Principal texture direction Saliency map 

Notes

References

  1. 1.
    Burger W, Burge MJ (2009) Principals of digital image processing: core algorithms springer. SpringerGoogle Scholar
  2. 2.
    Campisi P, Neri A, Panci G, Scarano G (2004) Robust rotation-invariant texture classification using a model based approach. IEEE Trans Image Process 13 (6):782–791CrossRefGoogle Scholar
  3. 3.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell, PAMI 8(6):679–698CrossRefGoogle Scholar
  4. 4.
    Chan YK, Chen CY (2004) Image retrieval system based on color-complexity and color-spatial features. J Syst Softw 71(1):65–70CrossRefGoogle Scholar
  5. 5.
    Dimitrovski I, Kocev D, Loskovska S (2016) Improving bag-of-visual-words image retrieval with predictive clustering trees. Inf Sci 329:851–865CrossRefGoogle Scholar
  6. 6.
    Do MN, Vetterli M (2002) Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden markov models. IEEE Trans Multimed 4(4):517–527CrossRefGoogle Scholar
  7. 7.
    Dos S, Miranda M, Edleno Silva de M, Soares da Silva A, da Silva Torres R (2017) Color and texture applied to a signature-based bag of visual words method for image retrieval. Multimed Tools Appl 76(15):16855–16872CrossRefGoogle Scholar
  8. 8.
    ElAlami ME (2011) A novel image retrieval model based on the most relevant features. Knowl-Based Syst 24(1):23–32CrossRefGoogle Scholar
  9. 9.
    ElAlami ME (2014) A new matching strategy for content based image retrieval system. Appl Soft Comput 14(Part C):407–418CrossRefGoogle Scholar
  10. 10.
    Everingham M, Van LG, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338CrossRefGoogle Scholar
  11. 11.
    Gong Y, Zhang H, Chuan HC, Sakauchi M (1994) An image database system with content capturing and fast image indexing abilities. In: 1994 Proceedings of IEEE international conference on multimedia computing and systems, pp 121–130Google Scholar
  12. 12.
    Gudivada VN, Raghavan VV (1995) Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans Inf Syst 13(2):115–144CrossRefGoogle Scholar
  13. 13.
    Guo JM, Prasetyo H (2015) Content-based image retrieval using features extracted from halftoning-based block truncation coding. IEEE Trans Image Process 24(3):1010–1024MathSciNetCrossRefGoogle Scholar
  14. 14.
    Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using lbp variance (lbpv) with global matching. Pattern Recogn 43(3):706–719zbMATHCrossRefGoogle Scholar
  15. 15.
    Gupta RD, Dash JK, Mukhopadhyay S (2013) Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis. Pattern Recogn 46(12):3256–3267zbMATHCrossRefGoogle Scholar
  16. 16.
    Huang PW, Dai SK (2003) Image retrieval by texture similarity. Pattern Recogn 36(3):665–679CrossRefGoogle Scholar
  17. 17.
    Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 762–768Google Scholar
  18. 18.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  19. 19.
    Jafari-Khouzani K, Soltanian-Zadeh H (2005) Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans Pattern Anal Mach Intell 27(6):1004–1008CrossRefGoogle Scholar
  20. 20.
    Ko B, Byun H (2005) Frip: a region-based image retrieval tool using automatic image segmentation and stepwise boolean and matching. IEEE Trans Multimed 7 (1):105–113CrossRefGoogle Scholar
  21. 21.
    Kokare M, Biswas PK, Chatterji BN (2005) Texture image retrieval using new rotated complex wavelet filters. IEEE Trans Syst Man Cybern Part B Cybern 35 (6):1168–1178CrossRefGoogle Scholar
  22. 22.
    Kurtz C, Depeursinge A, Napel S, Christopher FB, Rubin DL (2014) On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Med Image Anal 18(7):1082–1100CrossRefGoogle Scholar
  23. 23.
    Liapis S, Tziritas G (2004) Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans Multimed 6(5):676–686CrossRefGoogle Scholar
  24. 24.
    Lin CH, Chen RT, Chan YK (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665CrossRefGoogle Scholar
  25. 25.
    Liu GH, Li ZY, Zhang L, Xu Y (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133. Computer Analysis of Images and PatternsCrossRefGoogle Scholar
  26. 26.
    Liu GH, Yang JY (2008) Image retrieval based on the texton co-occurrence matrix. Pattern Recogn 41(12):3521–3527zbMATHCrossRefGoogle Scholar
  27. 27.
    Liu GH, Zhang L, Hou YK, Li ZY, Yang JY (2010) Image retrieval based on multi-texton histogram. Pattern Recogn 43(7):2380–2389zbMATHCrossRefGoogle Scholar
  28. 28.
    Luo J, Crandall D (2006) Color object detection using spatial-color joint probability functions. IEEE Trans Image Process 15(6):1443–1453CrossRefGoogle Scholar
  29. 29.
    Mahani N, Moghadam MK, Nezamabadi H (2012) A fuzzy difference based edge detector. Iranian J Fuzzy Syst 9(6):69–85Google Scholar
  30. 30.
    Manjunath B (2002) Introduction to MPEG-7. Wiley, New YorkGoogle Scholar
  31. 31.
    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842CrossRefGoogle Scholar
  32. 32.
    Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715CrossRefGoogle Scholar
  33. 33.
    Mehtre BM, Kankanhalli MS, Lee WF (1997) Shape measures for content based image retrieval A comparison. Inf Process Manag 33(3):319–337CrossRefGoogle Scholar
  34. 34.
    Mezaris V, Kompatsiaris I, Strintzis MG (2004) Region-based image retrieval using an object ontology and relevance feedback. EURASIP J Appl Signal Process 2004:886–901Google Scholar
  35. 35.
    Milanese R, Cherbuliez M (1999) A rotation, translation, and scale-invariant approach to content-based image retrieval. J Vis Commun Image Represent 10(2):186–196CrossRefGoogle Scholar
  36. 36.
    Min R, Cheng HD (2009) Effective image retrieval using dominant color descriptor and fuzzy support vector machine. Pattern Recogn 42(1):147–157zbMATHCrossRefGoogle Scholar
  37. 37.
    Moghaddam HA, Khajoie TT, Rouhi AH, Tarzjan MS (2005) Wavelet correlogram: a new approach for image indexing and retrieval. Pattern Recogn 38 (12):2506–2518CrossRefGoogle Scholar
  38. 38.
    Nene SA, Nayar SK, Murase H (1996) Columbia object image library (coil-100). Technical Report CUCS 6:06–96Google Scholar
  39. 39.
    Nezamabadi-pour H, Kabir E (2004) Image retrieval using histograms of uni-color and bi-color blocks and directional changes in intensity gradient. Pattern Recogn Lett 25(14):1547–1557CrossRefGoogle Scholar
  40. 40.
    Otsu N (1997) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(6):62–99Google Scholar
  41. 41.
    Palm C (2004) Color texture classification by integrative co-occurrence matrices. Pattern Recogn 37(5):965–976CrossRefGoogle Scholar
  42. 42.
    Pass G, Zabih R (1999) Comparing images using joint histograms. Multimed Syst 7(3):234–240CrossRefGoogle Scholar
  43. 43.
    Raghuwanshi G, Tyagi V (2016) Texture image retrieval using adaptive tetrolet transforms. Digital Signal Process 48(Supplement C):50–57MathSciNetCrossRefGoogle Scholar
  44. 44.
    Rajapakse J (2002) Adaptive blind signal and image processing: learning algorithms and applications. Neurocomputing 49(1-4):439–443CrossRefGoogle Scholar
  45. 45.
    Rao MB, Rao BP, Govardhan A (2011) Ctdcirs: content based image retrieval system based on dominant color and texture features. Int J Comput Appl 18(6):40–46Google Scholar
  46. 46.
    Rui Y, Huang TS, Chang SF (1999) Image retrieval: current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39–62CrossRefGoogle Scholar
  47. 47.
    Shahbahrami A, Borodin D, Juurlink B (2008) Comparison between color and texture features for image retrieval. In: Proceedings of 19th annual workshop on circuits systems and signal processingGoogle Scholar
  48. 48.
    Shrivastava N, Tyagi V (2014) Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Inform Sci 259 (Supplement C):212–224CrossRefGoogle Scholar
  49. 49.
    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. Special issue on Image and Video Processing Special issue on Recent Trends in Communications and Signal ProcessingCrossRefGoogle Scholar
  50. 50.
    Sun J, Zhang X, Cui J, Zhou L (2006) Image retrieval based on color distribution entropy. Pattern Recogn Lett 27(10):1122–1126CrossRefGoogle Scholar
  51. 51.
    Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32CrossRefGoogle Scholar
  52. 52.
    Torre V, Poggio TA (1986) On edge detection. IEEE Trans Pattern Anal Mach Intell, PAMI 8(2):147–163CrossRefGoogle Scholar
  53. 53.
    Varish N, Pal AK (2015) Content based image retrieval using statistical features of color histogram. In: 2015 3rd international conference on signal processing, communication and networking (ICSCN), pp 1–6Google Scholar
  54. 54.
    Varish N, Pradhan J, Pal AK (2017) Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform. Multimed Tools Appl 76(14):15885–15921CrossRefGoogle Scholar
  55. 55.
    Wang L, Healey G (1998) Using zernike moments for the illumination and geometry invariant classification of multispectral texture. IEEE Trans Image Process 7 (2):196–203CrossRefGoogle Scholar
  56. 56.
    Wang M, Song T (2013) Remote sensing image retrieval by scene semantic matching. IEEE Trans Geosci Remote Sens 51(5):2874–2886CrossRefGoogle Scholar
  57. 57.
    Youssef SM (2012) Ictedct-cbir: integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput Electr Eng 38(5):1358–1376. Special issue on Recent Advances in Security and Privacy in Distributed Communications and Image processingCrossRefGoogle Scholar
  58. 58.
    Zeng S, Huang R, Wang H, Kang Z (2016) Image retrieval using spatiograms of colors quantized by gaussian mixture models. Neurocomputing 171 (Supplement C):673–684CrossRefGoogle Scholar
  59. 59.
    Zhang R, Lin L, Zhang R, Zuo W, Zhang L (2015) Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans Image Process 24(12):4766–4779MathSciNetCrossRefGoogle Scholar
  60. 60.
    Zhou XS, Rui Y, Huang TS (1999) Water-filling: a novel way for image structural feature extraction. In: Proceedings 1999 international conference on image processing (Cat. 99CH36348), vol 2, pp 570–574Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringIndian Institute of Technology (ISM)DhanbadIndia

Personalised recommendations