Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix

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A.Obulesu 1,* V.Vijaya Kumar 1 L. Sumalatha 2

1. Anurag Group of Institutions (Autonomous), Hyderabad,India

2. JNTUK, Kakinada, Andhra Pradesh, India

* Corresponding author.


Received: 29 Sep. 2017 / Revised: 19 Oct. 2017 / Accepted: 7 Nov. 2017 / Published: 8 Apr. 2018

Index Terms

Peano scan, Left most, Bottom right most, Concatenation


In this paper, two extended versions of motif co-occurrence matrices (MCM) are derived and concatenated for efficient content-based image retrieval (CBIR). This paper divides the image into 2 x 2 grids. Each 2 x 2 grid is replaced with two different Peano scan motif (PSM) indexes, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. This transforms the entire image into two different images and co-occurrence matrices are derived on these two transformed images:  the first one is named as “motif co-occurrence matrix initiated from top left most pixel (MCMTL)” and second one is named as “motif co-occurrence matrix initiated from bottom right most pixel (MCMBR)”. The proposed method concatenates the feature vectors of MCMTL and MCMBR and derives multi motif co-occurrence matrix (MMCM) features. This paper carried out investigation on image databases i.e. Corel-1k, Corel-10k, MIT-VisTex, Brodtaz, and CMU-PIE and the results are compared with other well-known CBIR methods. The results indicate the efficacy of the proposed MMCM than the other methods and especially on MCM [19] method.

Cite This Paper

A.Obulesu, V. Vijay Kumar, L. Sumalatha," Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.4, pp. 59-72, 2018. DOI: 10.5815/ijigsp.2018.04.07


[1]F. Monay, D. Gatica-perez, Modeling semantic aspects for cross-media image indexing, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (10) (2007) 1802–1817.

[2]Pranoti P. Mane,  Amruta B. Rathi,  Narendra G. Bawane,  An Interactive Approach for Retrieval of Semantically Significant Images , I.J. Image, Graphics and Signal Processing, 2016, 3, 63-70

[3]R.W. Picard, F. Liu, A new ordering for image similarity, in: Proceedings of IEEE Conference on ASSP, April (1994) 129–132.

[4]M.J. Swain, D.H. Ballard, Color indexing, International Journal of Computer Vision 7 (1) (1991).

[5]G. Pass, R. Zahib, J. Miller, Comparing images using color coherent vector, in: The Fourth ACM International Multimedia Conference, November (1996) 18–22.

[6]Lakhdar Belhallouche ,,  Kamel Belloulata, Kidiyo Kpalma , A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform,  I.J. Image, Graphics and Signal Processing, 2016, 1, 1-14.

[7]Pranoti P. Mane ,   Narendra G. Bawane , Image Retrieval by Utilizing Structural Connections within an Image ,  I.J. Image, Graphics and Signal Processing, 2016, 1, 68-74.

[8]J. Huang, R. Kumar, M. Mitra, W. Zhu, W. Zahib, Image indexing using color correlogram, in: IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, June (1997) 762–768.

[9]P. Chang, J. Krumm, Object recognition with color co-occurrence histogram, in: Proceedings of the IEEE International Conference on CVPR, Fort Collins, CO (1997) 498–504.

[10]R. Haralick, K. Shanmugan, I. Dinstain, Textural features for image classification, IEEE Trans. Syst. Man Cybern. 3 (6) (1973) 610–622.

[11]J. Chen, S. Shan, C. He, G. Zhao, M. Pietikäinen, X. Chen, W. Gao, WLD: a robust local image descriptor, IEEE Trans. Pattern Anal. Mach. Intell. 32 (9) (2009) 1705–1720.

[12]L. Zhang, Z. Zhou, H. Li, Binary Gabor pattern: an efficient and robust descriptor for texture classification, in: 19th IEEE International Conference on Image Processing (ICIP), 2012, pp. 81–84.

[13]R. Haralick, Statistical and structural approaches to texture, Proc. IEEE 67 (5) (1979) 786–804.

[14]F.R. Siqueira, W.R. Schwartz, H. Pedrini, Multi-scale gray level co-occurrence matrices for texture description, Neuro computing 120 (2013) 336–345.

[15]M. Verma, B. Raman, S. Murala, Local extrema co-occurrence pattern for color and texture image retrieval, Neuro computing 165 (2015) 255–269.

[16]C.H. Liu, Z. Lei, Y.K. Hou, Z.Y. Li, J.Y. Yang, Image retrieval based on multi-textonhistogram, Pattern Recognit. 43 (2010) 2380–2389.

[17]S. Murala, Q.M. Jonathan, R.P. Maheshwari, R. Balasubramanian, Modified color motif co-occurrence matrix for image indexing and retrieval, Comput. Electr.Eng. 39 (2013) 762–774.

[18]S. Murala, R.P. Maheshwari, R. Balasubramanian, Expert system design using wavelet and color vocabulary trees for image retrieval, Int. J. Expert Syst. Appl.39 (2012) 5104–5114.

[19]Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B. Content-based image retrieval using motif co-occurrence matrix. Image Vision Comput 2004; 22:1211–20.

[20]C.H. Lin, R.T. Chen, Y.K.A. Chan, Smart content-based image retrieval systembased on color and texture feature, Image Vis. Comput. 27 (2009) 658–665.

[21]Vadivel, A.K. ShamikSural, Majumdar, An integrated color and intensity co-occurrence matrix, Pattern Recognit. Lett. 28 (2007) 974–983.

[22]M.N. Do, M. Vetterli, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-leibler distance, IEEE Trans. Image Process. 11, (2) (2002) 146–158.

[23]K. Prasanthi Jasmine1 ; P. Rajesh Kumar2 , Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image RetrievalM. I.J. Image, Graphics and Signal Processing, 2014, 9, 1-10.

[24]T.V. Madhusudhana Rao, Dr. S.Pallam Setty, Dr. Y.Srinivas,  An Efficient System for Medical Image Retrieval using Generalized Gamma Distribution, I.J. Image, Graphics and Signal Processing, 2015, 6, 52-58.

[25]M. Kokare, P.K. Biswas, B.N. Chatterji, Texture image retrieval using rotated wavelet filters, J. Pattern Recognit. Lett. 28 (2007) 1240–1249.

[26]J.C. Felipe, A.J.M. Traina, C.J. Traina, Retrieval by content of medical images using texture for tissue identification, in:  Proceedings of the 16th IEEE Symposium on Computer-Based Medical Systems, New York, USA, 2003, pp. 175–180.

[27]W. Liu, H. Zhang, Q. Tong, Medical image retrieval based on nonlinear texture features, Biomed. Eng. Instrum. Sci. 25 (1) (2008) 35–38.

[28]B. Ramamurthy, K.R. Chandran, V.R. Meenakshi, V. Shilpa, CBMIR: content based medical image retrieval system using texture and intensity for dental images, Commun. Comput. Inform. Sci. 305 (2012) 125–134.

[29]L. Nanni, S. Brahnam, A. Lumini, A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states, Expert Syst. Appl. 37 (12) (2010) 7888–7894.

[30]L. Nanni, A. Lumini, S. Brahnam, Local binary patterns variants as texture descriptors for medical image analysis, Artif. Intell. Med. 49 (2) (2010) 117– 125.

[31]Abbas H. Hassin Alasadi,  Saba Abdual Wahid, Effect of Reducing Colors Number on the Performance of CBIR System ,  I.J. Image, Graphics and Signal Processing (IJIGSP), 2016, 9, 10-16 

[32]Abdelhamid Abdesselam,  Edge Information for Boosting Discriminating Power of Texture Retrieval Techniques s and techniques, I.J. Image, Graphics and Signal Processing (IJIGSP), 2016, 4, 16-28.

[33]Lempel, J. Ziv, Compression of two-dimensional data, IEEE Transactions on Information Theory 32 (1) (1986) 2–8.

[34]T. Ojala, M. Pietikainen, D. Harwood, A comparative study of texture measures with classification based on feature distributions, Pattern Recogn. 29 (1) (1996) 51–59.

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

[36]Oliver, X. Lladó, J. Freixenet, J. Martí, False positive reduction in mammographic mass detection using local binary patterns, in: Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI 2007), Brisbane, Australia: Springer, Lecture Notes in Computer Science (LNCS) 4791, pp. 286–293, 2007.

[37]G. Seetharaman, B. Zavidovique, Image processing in a tree of Peano ,coded images, in: Proceedings of the IEEE Workshop on Computer Architecture for Machine Perception, Cambridge, CA (1997).

[38]G. Seetharaman, B. Zavidovique, Z-trees: adaptive pyramid algorithms for image segmentation, in: Proceedings of the IEEE International Conference on Image Processing, ICIP98, Chicago, IL, October (1998).

[39]Giancarlo R, Scaturro D, Utro F. Textual Data Compression In Computational Biology:A synopsis. Bioinformatics. 2009;25:1575–1586. 

[40]A.R. Butz, Space filling curves and mathematical programming, Information and Control 12 (1968) 314–330.

[41]Corel Photo Collection Color Image Database, online available on

[42]Corel database:

[43]MIT Vision and Modeling Group, Cambridge, „Vision texture‟,

[44]P. Brodatz, Textures: “A Photographic Album for Artists and Designers “.New York, NY, USA: Dover, 1999.

[45]T. Sim, S. Baker, and M. Bsat, ―The CMU pose, illumination, and expression database,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1615–1618, Dec. 2003. 

[46]X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE. Trans. Image Process., vol. 19, no. 6, pp. 1635–1650, Jun. 2010.

[47]K. Jeong, J. Choi, and G. Jang, “Semi-Local Structure Patterns for Robust Face Detection,” IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1400-1403, 2015.

[48]B. Zhang, Y. Gao, S. Zhao and J. Liu, “Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533-544, 2010.

[49]S. Murala, R.P. Maheshwari and R. Balasubramanian, “Local tetra patterns: a new feature descriptor for content-based image retrieval,” IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874-2886, 2012.