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

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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.

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References

  1. Burger W, Burge MJ (2009) Principals of digital image processing: core algorithms springer. Springer

  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):782–791

    Article  Google Scholar 

  3. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell, PAMI 8(6):679–698

    Article  Google Scholar 

  4. Chan YK, Chen CY (2004) Image retrieval system based on color-complexity and color-spatial features. J Syst Softw 71(1):65–70

    Article  Google Scholar 

  5. Dimitrovski I, Kocev D, Loskovska S (2016) Improving bag-of-visual-words image retrieval with predictive clustering trees. Inf Sci 329:851–865

    Article  Google Scholar 

  6. Do MN, Vetterli M (2002) Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden markov models. IEEE Trans Multimed 4(4):517–527

    Article  Google Scholar 

  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):16855–16872

    Article  Google Scholar 

  8. ElAlami ME (2011) A novel image retrieval model based on the most relevant features. Knowl-Based Syst 24(1):23–32

    Article  Google Scholar 

  9. ElAlami ME (2014) A new matching strategy for content based image retrieval system. Appl Soft Comput 14(Part C):407–418

    Article  Google Scholar 

  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):303–338

    Article  Google Scholar 

  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 121–130

  12. Gudivada VN, Raghavan VV (1995) Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans Inf Syst 13(2):115–144

    Article  Google Scholar 

  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):1010–1024

    Article  MathSciNet  Google Scholar 

  14. Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using lbp variance (lbpv) with global matching. Pattern Recogn 43(3):706–719

    Article  MATH  Google Scholar 

  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):3256–3267

    Article  MATH  Google Scholar 

  16. Huang PW, Dai SK (2003) Image retrieval by texture similarity. Pattern Recogn 36(3):665–679

    Article  Google Scholar 

  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 762–768

  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):1254–1259

    Article  Google Scholar 

  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):1004–1008

    Article  Google Scholar 

  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):105–113

    Article  Google Scholar 

  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):1168–1178

    Article  Google Scholar 

  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):1082–1100

    Article  Google Scholar 

  23. Liapis S, Tziritas G (2004) Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans Multimed 6(5):676–686

    Article  Google Scholar 

  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):658–665

    Article  Google Scholar 

  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 Patterns

    Article  Google Scholar 

  26. Liu GH, Yang JY (2008) Image retrieval based on the texton co-occurrence matrix. Pattern Recogn 41(12):3521–3527

    Article  MATH  Google Scholar 

  27. Liu GH, Zhang L, Hou YK, Li ZY, Yang JY (2010) Image retrieval based on multi-texton histogram. Pattern Recogn 43(7):2380–2389

    Article  MATH  Google Scholar 

  28. Luo J, Crandall D (2006) Color object detection using spatial-color joint probability functions. IEEE Trans Image Process 15(6):1443–1453

    Article  Google Scholar 

  29. Mahani N, Moghadam MK, Nezamabadi H (2012) A fuzzy difference based edge detector. Iranian J Fuzzy Syst 9(6):69–85

    Google Scholar 

  30. Manjunath B (2002) Introduction to MPEG-7. Wiley, New York

    Google Scholar 

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

    Article  Google Scholar 

  32. Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715

    Article  Google Scholar 

  33. Mehtre BM, Kankanhalli MS, Lee WF (1997) Shape measures for content based image retrieval A comparison. Inf Process Manag 33(3):319–337

    Article  Google Scholar 

  34. Mezaris V, Kompatsiaris I, Strintzis MG (2004) Region-based image retrieval using an object ontology and relevance feedback. EURASIP J Appl Signal Process 2004:886–901

    Google Scholar 

  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):186–196

    Article  Google Scholar 

  36. Min R, Cheng HD (2009) Effective image retrieval using dominant color descriptor and fuzzy support vector machine. Pattern Recogn 42(1):147–157

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  38. Nene SA, Nayar SK, Murase H (1996) Columbia object image library (coil-100). Technical Report CUCS 6:06–96

    Google Scholar 

  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):1547–1557

    Article  Google Scholar 

  40. Otsu N (1997) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(6):62–99

    Google Scholar 

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

    Article  Google Scholar 

  42. Pass G, Zabih R (1999) Comparing images using joint histograms. Multimed Syst 7(3):234–240

    Article  Google Scholar 

  43. Raghuwanshi G, Tyagi V (2016) Texture image retrieval using adaptive tetrolet transforms. Digital Signal Process 48(Supplement C):50–57

    Article  MathSciNet  Google Scholar 

  44. Rajapakse J (2002) Adaptive blind signal and image processing: learning algorithms and applications. Neurocomputing 49(1-4):439–443

    Article  Google Scholar 

  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):40–46

    Google Scholar 

  46. Rui Y, Huang TS, Chang SF (1999) Image retrieval: current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39–62

    Article  Google Scholar 

  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 processing

  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):212–224

    Article  Google Scholar 

  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 Processing

    Article  Google Scholar 

  50. Sun J, Zhang X, Cui J, Zhou L (2006) Image retrieval based on color distribution entropy. Pattern Recogn Lett 27(10):1122–1126

    Article  Google Scholar 

  51. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32

    Article  Google Scholar 

  52. Torre V, Poggio TA (1986) On edge detection. IEEE Trans Pattern Anal Mach Intell, PAMI 8(2):147–163

    Article  Google Scholar 

  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 1–6

  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):15885–15921

    Article  Google Scholar 

  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):196–203

    Article  Google Scholar 

  56. Wang M, Song T (2013) Remote sensing image retrieval by scene semantic matching. IEEE Trans Geosci Remote Sens 51(5):2874–2886

    Article  Google Scholar 

  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 processing

    Article  Google Scholar 

  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):673–684

    Article  Google Scholar 

  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):4766–4779

    Article  MathSciNet  Google Scholar 

  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 570–574

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  1. Department of Computer Science & Engineering, Indian Institute of Technology (ISM), Dhanbad, India

    Jitesh Pradhan, Arup Kumar Pal & Haider Banka

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  1. Jitesh Pradhan
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  2. Arup Kumar Pal
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Pradhan, J., Pal, A.K. & Banka, H. Principal texture direction based block level image reordering and use of color edge features for application of object based image retrieval. Multimed Tools Appl 78, 1685–1717 (2019). https://doi.org/10.1007/s11042-018-6246-4

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