• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Color and cross diagonal symmetric pair co-occurrence matrix
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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i27.712

Year: 2020, Volume: 13, Issue: 27, Pages: 2755-2773

Original Article

Color and cross diagonal symmetric pair co-occurrence matrix

B Srinivas1*, V Venkata Krishna2, L Sumalatha3

1Research Scholar, JNTU Kakinada, Asst. Professor, Anurag Group of Institutions (Autonomous), Hyderabad, Telangana, India
2Professor, Vasavi College of Engineering, Hyderabad, Telangana, India
3Professor, University College of Engineering, JNTUK, Kakinada, Andhra Pradesh, India

*Corresponding author
Email: [email protected]

Received Date:25 May 2020, Accepted Date:08 July 2020, Published Date:31 July 2020

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The main goal of this study is to derive an efficient feature vector to capture both the color and texture information. This makes the proposed descriptor as a multipurpose descriptor. Methodology: This study derived color information by combining the individual histograms of H, S and V plane. A texture descriptor is derived by considering the relationship: i) between pairs of symmetric cross pixels and ii) between pairs of symmetric diagonal pixels of the 3 x 3 neighborhood. The relationship is derived using XOR and AND logical functions. Findings: This study derived a unique six bit code and constructed a co-occurrence matrix. The GLCM features are derived on this. To reduce the complexity, this study also derived another descriptor by indexing the six bit code using the rotational invariant property. Novelty: The proposed color and texture descriptors give better results, and a significant improvement is achieved in this study by concatenating these two descriptors for CBIR.

Keywords: CBIR; firstorder; symmetric; derivatives; histogram; indexed; GLCM

References

  1. Zhang D, Islam MM, Lu G. A review on automatic image annotation techniques. Pattern Recognition. 2012;45(1):346–362. Available from: https://dx.doi.org/10.1016/j.patcog.2011.05.013
  2. Maillot N, Thonnat M, Boucher A. Towards ontology-based cognitive vision. Machine Vision and Applications. 2004;16:33–40. Available from: https://dx.doi.org/10.1007/s00138-004-0142-9
  4. Zagoris K, Ergina K, Papamarkos N. Image retrieval systems based on compact shape descriptor and relevance feedback information. Journal of Visual Communication and Image Representation. 2011;22:378–390. Available from: https://dx.doi.org/10.1016/j.jvcir.2011.03.002
  6. Li Y, Zhou C, Geng B, Xu C, Liu H. A comprehensive study on learning to rank for content-based image retrieval. Signal Processing. 2013;93(6):1426–1434. Available from: https://dx.doi.org/10.1016/j.sigpro.2012.06.012
  7. Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002;24(7):971–987. Available from: https://dx.doi.org/10.1109/tpami.2002.1017623
  8. Wang X, Wang Z. A novel method for image retrieval based on structure elements’ descriptor. Journal of Visual Communication and Image Representation. 2013;24(1):63–74. Available from: https://dx.doi.org/10.1016/j.jvcir.2012.10.003
  9. Tan X, Triggs B. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. IEEE Transactions on Image Processing. 2010;19(6):1635–1650. Available from: https://dx.doi.org/10.1109/tip.2010.2042645
  10. Reddy S, K, Kumar V, V, Krishna V, V. Face Recognition using Multi Region Prominent LBP Representation. International Journal of Electrical and Computer Engineering (IJECE). December. 2016;6(6).
  11. Bindumadhavi G, VK, Sasidhar K. Content Based Image Retrieval using Robust Local Octal Pattern (RLOPM) International Journal of Advanced Science and Technology. 2019;127:924–949.
  12. Reddy S, Kumar VV, Kumar APS. Classification of textures using a new descriptor circular and elliptical-LBP (CE-ELBP) International Journal of Applied Engineering Research. 2017;12(19):8844–8853.
  14. Srinivas B, Krishna VV, Sumalatha L. Advanced Local Direction Cross Diagonal Matrix. International Journal of Control and Automation (IJCA). 2019;12(6):795–819. Available from: http://sersc.org/journals/index.php/IJCA/article/view/4669
  15. Liu CWT, WC, CM. Adaptive color feature extraction based on image color distributions. IEEE Transactions on Image Processing. 2010;19(8):2005–2016.
  16. Manjunath BS, Ohm JR, Vasudevan VV, Yamada A. Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology. 2001;11:703–715. Available from: https://dx.doi.org/10.1109/76.927424
  17. Lee SH, Choi JY, Ro YM, Plataniotis KN. Local color vector binary patterns from multichannel face images for face recognition. IEEE Transactions on Image Processing. 2012;21(4):2347–2353. Available from: https://doi.org/10.1109/TIP.2011.2181526
  18. Bindumadhavi G, Kumar VV, Sasidhar K. Image Retrieval based on Color and Full Texton Matrix Histogram (C&FTMH) Features. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2019;8(8):507–521. Available from: https://www.ijitee.org/wp-content/uploads/papers/v8i8/H6681068819.pdf
  19. Nan B, Xu Y, Mu Z, Chen L. Content-based image retrieval using local texture-based color histogram. Proceedings of IEEE 2nd International Conference on Cybernetics (CYBCONF). 2015;p. 399–405. Available from: https://doi.org/10.1109/CYBConf.2015.7175967
  20. Dubey SR, Singh SK, Singh RK. Multichannel decoded local binary patterns for content-based image retrieval. IEEE Transactions on Image Processing. 2016;25(9):4018–4032. Available from: https://doi.org.10.1109/TIP.2016.2577887
  22. Liu L, Yu M, Shao L. Multi view alignment hashing for efficient image search. IEEE Transactions on Image Processing. 2015;24(3):956–966. Available from: https://doi.org/10.1109/TIP.2015.2390975
  23. Liu L, Shao L. Sequential compact code learning for unsupervised image hashing. IEEE Transactions on Neural Networks and Learning Systems. 2016;27(12):2526–2536. Available from: https://doi.org/10.1109/TNNLS.2015.2495345
  24. Srinivas B, Krishna VV, Sumalatha L. Second order Derivative Cross Diagonal Matrix approach for CBIR. Journal of Critical Reviews. 2020;7(7):414–427. Available from: https://doi.org/10.31838/jcr.07.07.73
  25. Santos JMd, Moura ESd, Silva ASd, Torres RdS. Color and texture applied to a signature-based bag of visual words method for image retrieval. Multimedia Tools and Applications. 2017;76:16855–16872. Available from: https://dx.doi.org/10.1007/s11042-016-3955-4
  27. Liu P, Guo JM, Wu CY, Cai D. Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval. IEEE Transactions on Image Processing. 2017;26(12):5706–5717. Available from: https://dx.doi.org/10.1109/tip.2017.2736343
  28. Yu J, Yang X, Gao F, Tao D. Deep multimodal distance metric learning using click constraints for image ranking. IEEE Transactions on Cybernetics. 2017;47(12):4014–4024. Available from: https://doi.org/10.1109/TCYB.2016.2591583
  29. Mohan BV, Kumar V, V. Face Recognition Using Robust Fuzzy Based Fundamental Texture Matrix (RFFTM) Adv Research in Dynamical & Control Systems. 2018;10(15).
  30. Qayyum A, Anwar SM, Awais M, Majid M. Medical image retrieval using deep convolutional neural network. Neurocomputing. 2017;266:8–20. Available from: https://dx.doi.org/10.1016/j.neucom.2017.05.025
  31. Pang S, Orgun MA, Yu Z. A novel biomedical image indexing and retrieval system via deep preference learning. Computer Methods and Programs in Biomedicine. 2018;158:53–69. Available from: https://dx.doi.org/10.1016/j.cmpb.2018.02.003
  33. Mohan BV, Kumar VV. Face Recognition Based On Gradient Integrated Texton Matrix. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2019;8(7):2460–2469. Available from: https://www.ijitee.org/wp-content/uploads/papers/v8i7/E3167038519.pdf
  34. Kishore B, Kumar VV, Shylashree N. Local texton centre symmetric pattern matrix (LTCSPM) on Wavelet domain for texture classification. International Journal of Innovative Technology and Exploring Engineering. 2018;8(2S):440–445.
  35. Kumari YS, Kumar VV, Satyanarayana CH. Classification of Textures Based On Multi Block Local Texton Feature Model. Journal of Adv Research in Dynamical & Control Systems. 2018;10(1):336–347.
  36. Srinivas B, Krishna VV, Sumalatha L. A New Framework for CBIR Using Odd and Even Tetra Texton Matrix. International Journal of Advanced Science and Technology (IJAST). 2020;29(3):622–645. Available from: http://sersc.org/journals/index.php/IJAST/article/view/5746/3580
  37. Madhavi BB, Kumar VV, Sasidhar K. A New Frame Work for Content Based Image Retrieval Based on Rule Based Motifs on Full Texton Images. International Journal of Advanced Trends in Computer Science and Engineering. 2019;8:1083–1098. Available from: htps://doi.org/10.30534/ijatcse/2019/15842019
  38. Reddy AM, , Krishna VV, Sumalatha L. Face Recognition based on Cross Diagonal Complete Motif Matrix. International Journal of Image, Graphics and Signal Processing. 2018;10(3):59–66. Available from: https://dx.doi.org/10.5815/ijigsp.2018.03.07
  39. Obulesu A, , Kumar VV, Sumalatha L. Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix. International Journal of Image, Graphics and Signal Processing. 2018;10(4):59–72. Available from: https://dx.doi.org/10.5815/ijigsp.2018.04.07
  40. Heikkil M, Pietik M, Schmid C. Description of Interest Regions with Center-Symmetric Local Binary Patterns. Computer Vision, Graphics and Image Processing. 2006;4338:58–69. Available from: https://doi.org/10.1007/11949619_6
  41. Srinivas J, Moizqyser AA, Reddy E, B. Classification of Textures Based on Weighted and Robust Circular Symmetric Local Binary Patterns. Jour of Adv Research in Dynamical & Control Systems. 2018;10(4):306–319.
  42. Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;SMC-3(6):610–621. Available from: https://dx.doi.org/10.1109/tsmc.1973.4309314
  46. Abdelmounaime S, Dong-Chen H. New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis. International Scholarly Research Notices Machine Vision. 2013;p. 1–14. Available from: https://doi.org/10.1155/2013/876386
  47. Sim T, Baker S, Bsat M. The CMU pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003;25(12):1615–1618. Available from: https://dx.doi.org/10.1109/tpami.2003.1251154
  48. Valtteri T, Timo A, Pietikainen M. Block-Based Methods for Image Retrieval Using Local Binary Patterns. Lecture Notes in Computer Science. 2005;3450:882–891. Available from: https://doi.org/10.1007/11499145_89

Copyright

© 2020 Srinivas, Venkata Krishna, Sumalatha. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).

  • 02 August 2020

Year: 2020, Volume: 13, Issue: 27

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