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Multi-level colored directional motif histograms for content-based image retrieval

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Abstract

Color features and local geometrical structures are the two basic image features which are sufficient to convey the image semantics. Both of these features show diverse nature on the different regions of a natural image. Traditional local motif patterns are standard tools to emphasize these local visual image features. These motif-based schemes consider either structural orientations or limited directional patterns which are not sufficient to realize the detailed local geometrical properties of an image. To address these issues, we have proposed a new multi-level colored directional motif histogram (MLCDMH) for devising a content-based image retrieval scheme. The proposed scheme extracts local structural features at three different levels. Initially, MLCDMH scheme extracts directional structural patterns from a \(3 \times 3\) pixel grids of an image. This reflects the \(9^9\) different structural arrangements using 28 directional patterns. Next, we have used a weighted neighboring similarity (WNS) scheme to exploit the uniqueness of each motif pixel in its local surrounding. The WNS scheme will compute the importance of each directional motif pattern in its \(3 \times 3\) local neighborhood. In the last level, we have fused all directional motif images into a single directional difference matrix which reflects the local structural and directional motif features in detail and also reduces the computation overhead. The MLCDMH considers all possible permutations and rotations of the motif patterns to generate rotational invariant structural features. The image retrieval performance of this proposed scheme has been evaluated using different Corel/natural, object, texture and heterogeneous image datasets. The results of the retrieval experiments have shown satisfactory improvement over other motif- and non-motif-based CBIR approaches.

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References

  1. Swain, M.J.: Interactive indexing into image databases. In: Proceedings of SPIE 1908, Storage and Retrieval for Image and Video Databases (1993). https://doi.org/10.1117/12.143659

  2. Worring, M., Smeulders, A.W., Gupta, A., Santini, S., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1349–1380 (2000)

    Google Scholar 

  3. Gudivada, V.N., Raghavan, V.V.: Content based image retrieval systems. Computer 28(9), 18–22 (1995)

    Google Scholar 

  4. Lin, C.-H., Chen, C.-C., Lee, H.-L., Liao, J.-R.: Fast k-means algorithm based on a level histogram for image retrieval. Expert Syst. Appl. 41(7), 3276–3283 (2014)

    Google Scholar 

  5. Anuar, F.M., Setchi, R., Lai, Y.: Trademark image retrieval using an integrated shape descriptor. Expert Syst. Appl. 40(1), 105–121 (2013)

    Google Scholar 

  6. Penatti, O.A.B., Valle, E., Torres, R.S.: Comparative study of global color and texture descriptors for web image retrieval. J. Vis. Commun. Image Represent. 23(2), 359–380 (2012)

    Google Scholar 

  7. Vipparthi, S.K., Nagar, S.K.: Expert image retrieval system using directional local motif XoR patterns. Expert Syst. Appl. 41(17), 8016–8026 (2014)

    Google Scholar 

  8. Varish, N., Pradhan, J., Pal, A.K.: Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform. Multimed. Tools Appl. 76(14), 15885–15921 (2017)

    Google Scholar 

  9. Chun, Y.D., Kim, N.C., Jang, I.H.: Content-based image retrieval using multiresolution color and texture features. IEEE Trans. Multimed. 10(6), 1073–1084 (2008)

    Google Scholar 

  10. Lu, Z.-M., Burkhardt, H.: Colour image retrieval based on DCT-domain vector quantisation index histograms. Electron. Lett. 41(1), 956–957 (2005)

    Google Scholar 

  11. Lu, T.-C., Chang, C.-C.: Color image retrieval technique based on color features and image bitmap. Inf. Process. Manag. 43(2), 461–472 (2007). Special issue on AIRS2005: Information Retrieval Research in Asia

    Google Scholar 

  12. Yue, J., Li, Z., Liu, L., Fu, Z.: Content-based image retrieval using color and texture fused features. Math. Comput. Model. 54(3), 1121–1127 (2011). Mathematical and Computer Modeling in agriculture (CCTA 2010)

    Google Scholar 

  13. Kokare, M., Biswas, P.K., Chatterji, B.N.: Texture image retrieval using rotated wavelet filters. Pattern Recognit. Lett. 28(10), 1240–1249 (2007)

    Google Scholar 

  14. Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C.: The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22(6), 123–151 (2005)

    Google Scholar 

  15. Krommweh, J.: Tetrolet transform: a new adaptive haar wavelet algorithm for sparse image representation. J. Vis. Commun. Image Represent. 21(4), 364–374 (2010)

    Google Scholar 

  16. Han, J., Ma, K.-K.: Rotation-invariant and scale-invariant gabor features for texture image retrieval. Image Vis. Comput. 25(9), 1474–1481 (2007)

    Google Scholar 

  17. He, Z., You, X., Yuan, Y.: Texture image retrieval based on non-tensor product wavelet filter banks. Signal Process. 89(8), 1501–1510 (2009)

    MATH  Google Scholar 

  18. Wang, X.-Y., Zhang, B.-B., Yang, H.-Y.: Content-based image retrieval by integrating color and texture features. Multimed. Tools Appl. 68(3), 545–569 (2014)

    Google Scholar 

  19. Li, C., Huang, Y., Zhu, L.: Color texture image retrieval based on Gaussian copula models of Gabor wavelets. Pattern Recognit. 64, 118–129 (2017)

    Google Scholar 

  20. Sadeghi, B., Jamshidi, K., Vafaei, A., Monadjemi, S.A.: A local image descriptor based on radial and angular gradient intensity histogram for blurred image matching. Vis. Comput. 35(10), 1373–1391 (2019). https://doi.org/10.1007/s00371-018-01616-z

    Article  Google Scholar 

  21. Jai, A.K., Vailay, A.: Shape-based retrieval: a case study with trademark image databases. Pattern Recognit. 31(9), 1369–1390 (1998)

    Google Scholar 

  22. Pradhan, J., Pal, A.K., Banka, H.: A prominent object region detection based approach for CBIR application. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 447–452. IEEE (2016)

  23. Won, C.S., Park, D.K., Park, S.-J.: Efficient use of MPEG-7 edge histogram descriptor. ETRI J. 24(1), 23–30 (2002)

    MathSciNet  Google Scholar 

  24. 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(2), 1685–1717 (2019). https://doi.org/10.1007/s11042-018-6246-4

    Article  Google Scholar 

  25. Chen, L., Wang, R., Yang, J., Xue, L., Hu, M.: Multi-label image classification with recurrently learning semantic dependencies. Vis. Comput. 35(10), 1361–1371 (2019). https://doi.org/10.1007/s00371-018-01615-0

    Article  Google Scholar 

  26. Liu, Y., Zhang, D., Lu, G., Ma, W.-Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognit. 40(1), 262–282 (2007)

    MATH  Google Scholar 

  27. An, F., Liu, Z.: Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM. Vis. Comput. 1–16 (2019). https://doi.org/10.1007/s00371-019-01635-4

  28. Mohammed, M.M., Badr, A., Abdelhalim, M.B.: Image classification and retrieval using optimized pulse-coupled neural network. Expert Syst. Appl. 42(11), 4927–4936 (2015)

    Google Scholar 

  29. Thuy, Q.D.T., Huu, Q.N., Van, C.P., Quoc, T.N.: An efficient semantic-related image retrieval method. Expert Syst. Appl. 72, 30–41 (2017)

    Google Scholar 

  30. Khatami, A., Babaie, M., Tizhoosh, H.R., Khosravi, A., Nguyen, T., Nahavandi, S.: A sequential search-space shrinking using cnn transfer learning and a radon projection pool for medical image retrieval. Expert Syst. Appl. 100, 224–233 (2018)

    Google Scholar 

  31. Cheng, S., Lai, H., Wang, L., Qin, J.: A novel deep hashing method for fast image retrieval. Vis. Comput. 35(9), 1255–1266 (2019)

    Google Scholar 

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

    Google Scholar 

  33. Takala, V., Ahonen, T., Pietikäinen, M.: Block-Based Methods for Image Retrieval Using Local Binary Patterns, pp. 882–891. Springer, Berlin (2005)

    Google Scholar 

  34. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009)

    MATH  Google Scholar 

  35. Subrahmanyam, M., Maheshwari, R.P., Balasubramanian, R.: Expert system design using wavelet and color vocabulary trees for image retrieval. Expert Syst. Appl. 39(5), 5104–5114 (2012)

    Google Scholar 

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

    Google Scholar 

  37. Bhunia, A.K., Bhattacharyya, A., Banerjee, P., Roy, P.P., Murala, S.: A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. arXiv preprint arXiv:1801.00879 (2018)

  38. Feng, Q., Hao, Q., Chen, Y., Yi, Y., Wei, Y., Dai, J.: Hybrid histogram descriptor: a fusion feature representation for image retrieval. Sensors 18(6), 1943 (2018)

    Google Scholar 

  39. Obulesu, A., Vijay Kumar, V., Sumalatha, L.: Content based image retrieval using multi motif co-occurrence matrix. Int. J. Image Graph. Signal Process. 10(4), 59 (2018)

    Google Scholar 

  40. Jhanwar, N., Chaudhuri, S., Seetharaman, G., Zavidovique, B.: Content based image retrieval using motif cooccurrence matrix. Image Vis. Comput. 22(14), 1211–1220 (2004). The Indian Conference on Vision, Graphics and Image Processing

    Google Scholar 

  41. Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 985–1002 (2008)

    Google Scholar 

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

    Google Scholar 

  43. tropical-fruits-db-1024x768.tar.gz. http://www.ic.unicamp.br/~rocha/pub/downloads/tropical-fruits-DB-1024x768.tar.gz/. Accessed 18 Aug 2017

  44. site www, vision & image, lagis-vi.univ-lille1.fr (2017). http://lagis-vi.univlille1.fr/datasets/outex.html. Accessed 18 Aug 2017

  45. Everingham, M., Van, L.G., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Google Scholar 

  46. Yao, C.-H., Chen, S.-Y.: Retrieval of translated, rotated and scaled color textures. Pattern Recognit. 36(4), 913–929 (2003)

    Google Scholar 

  47. Liu, G.-H., Zhang, L., Hou, Y.-K., Li, Z.-Y., Yang, J.-Y.: Image retrieval based on multi-texton histogram. Pattern Recognit. 43(7), 2380–2389 (2010)

    MATH  Google Scholar 

  48. Liu, G.-H., Li, Z.-Y., Zhang, L., Xu, Y.: Image retrieval based on micro-structure descriptor. Pattern Recognit. 44(9), 2123–2133 (2011). Computer Analysis of Images and Patterns

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  51. Galshetwar, G.M., Waghmare, L.M., Gonde, A.B., Murala, S.: Local energy oriented pattern for image indexing and retrieval. J. Vis. Commun. Image Represent. 64, 102615 (2019)

    Google Scholar 

  52. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556 (2014)

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

    Jitesh Pradhan, Ashok Ajad, Arup Kumar Pal & Haider Banka

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  1. Jitesh Pradhan
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Pradhan, J., Ajad, A., Pal, A.K. et al. Multi-level colored directional motif histograms for content-based image retrieval. Vis Comput 36, 1847–1868 (2020). https://doi.org/10.1007/s00371-019-01773-9

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