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Content-Based Image Retrieval Using Multiresolution Feature Descriptors

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Recent Advances in Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 804))

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Abstract

The advent of low-cost cameras and smartphones have made the task of image capturing quite easy nowadays. This has resulted in the collection of large number of unorganized images. Accessing images from large repository of unorganized images is quite challenging. There is a need of such systems which help in proper organization and easy access of images. The field of image retrieval, using text or image, attempts to solve this problem. While text-based retrieval systems are quite popular, they suffer from certain drawbacks. The other type of image retrieval system, which is Content-based Image Retrieval (CBIR) system, uses image features to search for relevant images. This chapter discusses the concept multiresolution feature descriptors for CBIR. For capturing varying level of details, single resolution processing of image proves to be insufficient. The use of multiresolution descriptors prove to be quite efficient in capturing complex foreground and background details in an image. This chapter discusses the important properties and advantages of multiresolution feature descriptors. Furthermore, this chapter proposes a CBIR technique using a novel multiresolution feature descriptor. The proposed method constructs feature vector by capturing shape feature in a localized manner. The experimental results show the effectiveness of the proposed method.

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References

  1. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 5.1–5.60 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D.: Query by image and video content: The QBIC system. Computer 28(9), 23–32 (1995)

    Article  Google Scholar 

  4. Smith, J.R., Chang, S.F.: Visualseek: a fully automated content-based image query system. In: Fourth ACM International Conference on Multimedia, pp. 87–98. ACM (1997)

    Google Scholar 

  5. Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)

    Article  Google Scholar 

  6. Smith, J.R., Chang, S.F.: Tools and techniques for color image retrieval. In: Storage and Retrieval for Still Image and Video Databases IV, vol. 2670, pp. 426–438. International Society for Optics and Photonics (1996)

    Google Scholar 

  7. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)

    Article  Google Scholar 

  8. Zhang, D., Wong, A., Indrawan, M., Lu, G.: Content-based image retrieval using Gabor texture features. In: IEEE PacificRim Conference on Multimedia, pp. 392–395. IEEE (2000)

    Google Scholar 

  9. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhang, D., Lu, G.: Shape-based image retrieval using generic Fourier descriptor. Signal Process.: Image Commun. 17(10), 825–848 (2002)

    Google Scholar 

  13. Li, S., Lee, M.C., Pun, C.M.: Complex zernike moments features for shape-based image retrieval. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 39(1), 227–237 (2009)

    Article  Google Scholar 

  14. Srivastava, P., Binh, N.T., Khare, A.: Content-based image retrieval using moments. In: International Conference on Context-Aware Systems and Applications, pp. 228–237. Springer (2013)

    Google Scholar 

  15. Memon, M.H., Memon, I., Li, J.P., Arain, Q.A.: IMRBS: image matching for location determination through a region-based similarity technique for CBIR. Int. J. Comput. Appl. 1–14 (2018)

    Google Scholar 

  16. Memon, M.H., Li, J.P., Memon, I., Arain, Q.A.: Geo matching regions: multiple regions of interests using content based image retrieval based on relative locations. Multimedia Tools Appl. 76(14), 15377–15411 (2017)

    Article  Google Scholar 

  17. Wang, X.Y., Yu, Y.J., Yang, H.Y.: An effective image retrieval scheme using color, texture and shape features. Comput. Stand. Interfaces 33(1), 59–68 (2011)

    Article  Google Scholar 

  18. Jain, A.K., Vailaya, A.: Image retrieval using color and shape. Pattern Recogn. 29(8), 1233–1244 (1996)

    Article  Google Scholar 

  19. Huang, Z.C., Chan, P.P., Ng, W.W., Yeung, D.S.: Content-based image retrieval using color moment and gabor texture feature. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 719–724. IEEE (2010)

    Google Scholar 

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

    Article  Google Scholar 

  21. Alzubi, A., Amira, A., Ramzan, N.: Semantic content-based image retrieval: a comprehensive study. J. Vis. Commun. Image Represent. 32, 20–54 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  23. Liu, G.H., Li, Z.Y., Zhang, L., Xu, Y.: Image retrieval based on micro-structure descriptor. Pattern Recogn. 44(9), 2123–2133 (2011)

    Article  Google Scholar 

  24. Liu, G.H., Yang, J.Y., Li, Z.: Content-based image retrieval using computational visual attention model. Pattern Recogn. 48(8), 2554–2566 (2015)

    Article  Google Scholar 

  25. Wan, J., Wang, D., Hoi, S.C.H., Wu, P., Zhu, J., Zhang, Y., Li, J.: Deep learning for content-based image retrieval: a comprehensive study. In: 22nd ACM International Conference on Multimedia, pp. 157–166. ACM (2014)

    Google Scholar 

  26. Awad, A.I., Hassaballah, M.: Image Feature Detectors and Descriptors. Springer (2016)

    Google Scholar 

  27. Li, J., Allinson, N.M.: A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12), 1771–1787 (2008)

    Article  Google Scholar 

  28. Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Fourth ACM International Conference on Multimedia, pp. 65–73. ACM (1997)

    Google Scholar 

  29. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 762–768. IEEE (1997)

    Google Scholar 

  30. Takala, V., Ahonen, T., Pietikäinen, M.: Block-based methods for image retrieval using local binary patterns. In: Scandinavian Conference on Image Analysis, pp. 882–891. Springer (2005)

    Google Scholar 

  31. Yuan, X., Yu, J., Qin, Z., Wan, T.: A SIFT-LBP image retrieval model based on bag of features. In: IEEE International Conference on Image Processing, pp. 1061–1064. IEEE (2011)

    Google Scholar 

  32. Yu, J., Qin, Z., Wan, T., Zhang, X.: Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing 120, 355–364 (2013)

    Article  Google Scholar 

  33. Srivastava, P., Khare, A.: Integration of wavelet transform, local binary patterns and moments for content-based image retrieval. J. Vis. Commun. Image Represent. 42, 78–103 (2017)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  35. Guo, Z., Zhang, L., Zhang, D., Mou, X.: Hierarchical multiscale LBP for face and palmprint recognition. In: 17th IEEE International Conference on Image Processing (ICIP), pp. 4521–4524. IEEE (2010)

    Google Scholar 

  36. Srivastava, P., Khare, A.: Utilizing multiscale local binary pattern for content-based image retrieval. Multimedia Tools Appl. 77(10), 12377–12403 (2018)

    Article  Google Scholar 

  37. Srivastava, P., Binh, N.T., Khare, A.: Content-based image retrieval using moments of local ternary pattern. Mobile Netw. Appl. 19(5), 618–625 (2014)

    Article  Google Scholar 

  38. Vipparthi, S.K., Nagar, S.: Directional local ternary patterns for multimedia image indexing and retrieval. Int. J. Signal Imaging Syst. Eng. 8(3), 137–145 (2015)

    Article  Google Scholar 

  39. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)

    Article  Google Scholar 

  40. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  MathSciNet  Google Scholar 

  41. Ledwich, L., Williams, S.: Reduced SIFT features for image retrieval and indoor localisation. In: Australian Conference on Robotics and Automation, vol. 322, p. 3 (2004)

    Google Scholar 

  42. Srivastava, P., Khare, A.: Content-based image retrieval using scale invariant feature transform and moments. In: IEEE International Conference on Electrical, Computer and Electronics Engineering (UPCON), pp. 162–166. IEEE (2016)

    Google Scholar 

  43. Srivastava, P., Khare, M., Khare, A.: Content-based image retrieval using scale invariant feature transform and gray level co-occurrence matrix. In: 2nd International Workshop on Pattern Recognition, vol. 10443, pp. 104430V-1–104430V-6. International Society for Optics and Photonics (2017)

    Google Scholar 

  44. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: European Conference on Computer Vision, pp. 404–417. Springer (2006)

    Google Scholar 

  45. Velmurugan, K., Baboo, L.D.S.S.: Content-based image retrieval using SURF and colour moments. Glob. J. Comput. Sci. Technol. (2011)

    Google Scholar 

  46. Huang, S., Cai, C., Zhao, F., He, D., Zhang, Y.: An efficient wood image retrieval using SURF descriptor. In: International Conference on Test and Measurement, vol. 2, pp. 55–58. IEEE (2009)

    Google Scholar 

  47. Teague, M.R.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70(8), 920–930 (1980)

    Article  MathSciNet  Google Scholar 

  48. Yu, H., Li, M., Zhang, H.J., Feng, J.: Color texture moments for content-based image retrieval. In: International Conference on Image Processing (ICIP), vol. 3, pp. 929–932. IEEE (2002)

    Google Scholar 

  49. Rao, C., Kumar, S.S., Mohan, B.C., et al.: Content based image retrieval using exact legendre moments and support vector machine. arXiv:1005.5437 (2010)

  50. Kim, W.Y., Kim, Y.S.: A region-based shape descriptor using zernike moments. Signal Process.: Image Commun. 16(1–2), 95–102 (2000)

    Google Scholar 

  51. Amanatiadis, A., Kaburlasos, V., Gasteratos, A., Papadakis, S.: Evaluation of shape descriptors for shape-based image retrieval. IET Image Process. 5(5), 493–499 (2011)

    Article  Google Scholar 

  52. Mandal, M.K., Aboulnasr, T., Panchanathan, S.: Image indexing using moments and wavelets. IEEE Trans. Consum. Electron. 42(3), 557–565 (1996)

    Article  Google Scholar 

  53. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  54. Junior, O.L., Delgado, D., Gonçalves, V., Nunes, U.: Trainable classifier-fusion schemes: an application to pedestrian detection. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITSC’09, pp. 1–6. IEEE (2009)

    Google Scholar 

  55. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  56. Starck, J.L., Candès, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  57. Ardizzoni, S., Bartolini, I., Patella, M.: Windsurf: region-based image retrieval using wavelets. In: International Workshop on Database and Expert Systems Applications, 1999, pp. 167–173. IEEE (1999)

    Google Scholar 

  58. Moghaddam, H.A., Khajoie, T.T., Rouhi, A.H., Tarzjan, M.S.: Wavelet correlogram: a new approach for image indexing and retrieval. Pattern Recogn. 38(12), 2506–2518 (2005)

    Article  Google Scholar 

  59. Loupias, E., Sebe, N.: Wavelet-based salient points: applications to image retrieval using color and texture features. In: International Conference on Advances in Visual Information Systems, pp. 223–232. Springer (2000)

    Google Scholar 

  60. Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized gaussian density and Kullback-Leibler distance. IEEE Trans. Image Process. 11(2), 146–158 (2002)

    Article  MathSciNet  Google Scholar 

  61. Moghaddam, H.A., Saadatmand-Tarzjan, M.: Gabor wavelet correlogram algorithm for image indexing and retrieval. In: 18th International Conference on Pattern Recognition ICPR, vol. 2, pp. 925–928. IEEE (2006)

    Google Scholar 

  62. Srivastava, P., Khare, A.: Content-based image retrieval using local binary curvelet co-occurrence pattern: a multiresolution technique. Comput. J. 61(3), 369–385 (2017)

    Article  Google Scholar 

  63. Murtagh, F., Starck, J.L.: Wavelet and curvelet moments for image classification: application to aggregate mixture grading. Pattern Recogn. Lett. 29(10), 1557–1564 (2008)

    Article  Google Scholar 

  64. Youssef, S.M.: 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 (2012)

    Article  Google Scholar 

  65. Khare, M., Srivastava, P., Gwak, J., Khare, A.: A multiresolution approach for content-based image retrieval using wavelet transform of local binary pattern. In: Asian Conference on Intelligent Information and Database Systems, pp. 529–538. Springer (2018)

    Google Scholar 

  66. Zhang, D., Islam, M.M., Lu, G., Sumana, I.J.: Rotation invariant curvelet features for region based image retrieval. Int. J. Comput. Vis. 98(2), 187–201 (2012)

    Article  MathSciNet  Google Scholar 

  67. Feng, L., Wu, J., Liu, S., Zhang, H.: Global correlation descriptor: a novel image representation for image retrieval. J. Vis. Commun. Image Represent. 33, 104–114 (2015)

    Article  Google Scholar 

  68. Zhang, M., Zhang, K., Feng, Q., Wang, J., Kong, J., Lu, Y.: A novel image retrieval method based on hybrid information descriptors. J. Vis. Commun. Image Represent. 25(7), 1574–1587 (2014)

    Article  Google Scholar 

  69. Srivastava, P., Prakash, O., Khare, A.: Content-based image retrieval using moments of wavelet transform. In: International Conference on Control, Automation and Information Sciences (ICCAIS), pp. 159–164. IEEE (2014)

    Google Scholar 

  70. Zeng, S., Huang, R., Wang, H., Kang, Z.: Image retrieval using spatiograms of colors quantized by gaussian mixture models. Neurocomputing 171, 673–684 (2016)

    Article  Google Scholar 

  71. Srivastava, P., Khare, A.: Content-based image retrieval using multiscale local spatial binary Gaussian co-occurrence pattern. In: Intelligent Communication and Computational Technologies, pp. 85–95. Springer (2018)

    Google Scholar 

  72. Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1075–1088 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. NIIT University, Neemrana, Rajasthan, India

    Prashant Srivastava

  2. Department of Electronics and Communication, University of Allahabad, Allahabad, UP, India

    Ashish Khare

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  1. Prashant Srivastava
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  2. Ashish Khare
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Correspondence to Prashant Srivastava .

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Editors and Affiliations

  1. Department of Computer Science, South Valley University, Luxor, Egypt

    Mahmoud Hassaballah

  2. Department of Information Technology, Zagazig University, Zagazig, Egypt

    Khalid M. Hosny

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Srivastava, P., Khare, A. (2019). Content-Based Image Retrieval Using Multiresolution Feature Descriptors. In: Hassaballah, M., Hosny, K. (eds) Recent Advances in Computer Vision. Studies in Computational Intelligence, vol 804. Springer, Cham. https://doi.org/10.1007/978-3-030-03000-1_9

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