Integration Colour and Texture Features for Content-based Image Retrieval

Full Text (PDF, 843KB), PP.10-18

Views: 0 Downloads: 0

Author(s)

Hanan A. Al-Jubouri 1,*

1. Mustansiriyah University/ Department of Computer Engineering, Baghdad, Iraq

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2020.02.02

Received: 18 Oct. 2019 / Revised: 20 Dec. 2019 / Accepted: 12 Jan. 2020 / Published: 8 Apr. 2020

Index Terms

Content-Based Image Retrieval (CBIR), Gary Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), data and score-level fusion.

Abstract

Content-Based Image Retrieval offers an automatic way to extract visual image contents such as colour, texture, and shape so-called extracted features. Due to growing volume of digital images, Content-Based Image Retrieval is emerged to store and retrieved images from large scale databases. However, Content-Based Image Retrieval faces a challenge of meaning “Semantic gap” between machine and human conceptual. How to reduce this gap between colour and/or texture features that represent an object in the image? It is still the challenge that basically related to the effectiveness of image representation by extracted features and similarity measures between a query image features and database image features. Hence, different visual features have been proposed such as Gary Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT) texture features that are extracted from gray-scale images. This paper presents an unsupervised algorithm that exploits data and score-level fusion to address the semantic gap. The algorithm first extracts mentioned features from colour images in HSV and YCbCr colour spaces to increase the effectiveness of image representation by integrating texture and colour visual information in terms of data-level fusion. Resulted similarity retrieval values are then fused in three versions of score-level fusion, summing values without weights, fixed, and adaptive weights using linear regression to raise relevant images in a ranked retrieved images list. WANG standard colour images are used to implement the algorithm. Rates of achievement in image retrievals are enhanced at both levels.

Cite This Paper

Hanan A. Al-Jubouri, " Integration Colour and Texture Features for Content-based Image Retrieval", International Journal of Modern Education and Computer Science(IJMECS), Vol.12, No.2, pp. 10-18, 2020.DOI: 10.5815/ijmecs.2020.02.02

Reference

[1]A. W. Smeulders et al., “Content-based image retrieval at the end of the early years, ” Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(12), pp. 1349-1380, 2000.
[2]R. Datta, D. Joshi, J. Li & J. Z. Wang, J. Z., “Image retrieval: Ideas, influences, and trends of the new age,” ACM Computing Surveys (CSUR), 40(2), p. 5E, 2008.
[3]Haralick, R. M., Shanmugam, K. & Dinstein, I. H. , “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), pp. 610-621, 1973.
[4]J. Prabhu and J. S. Kumar, “Wavelet based content based image retrieval using color and texture feature extraction by gray level co ocurence matrix and color coocurence matrix,” Journal of Computer Science, Science Publication 10 (1), p.15-22, 2014.
[5]N. Bagri and P. K. Johari, P., “A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval,” International Journal of Advanced Science and Technology Vol.80, ISSN: 2005-4238, pp.41-52.P, 2015.
[6]M. Benco, R. Hudec, P. Kamencay, M. Zachariasova, and S. Matuska, “An Advanced Approach to Extraction Color Texture Features Based on GLCM,” International Journal of Advanced Robotic Systems, ISSN 1729-8806, 2014.
[7]N. Puviarasan, R. Bhavani and A. Vasanthi, “mage Retrieval Using Combination of Texture and Shape Features,” International Journal of Advanced Research in Computer and Communication Engineering ,Vol. 3, Issue 3, 2014.
[8]R. Putri, H. Prabawa and Y. Wihardi, “Color and texture features extraction on content-based image retrieval,” 3rd International Conference on Science in Information Technology (ICSITech), IEEE, 2017.
[9]M. V. Lande, P. Bhanodiya and P. Jain, “An effective content-based image retrieval using color, texture and shape feature,” In Intelligent Computing, Networking, and Informatics(pp. 1163-1170). Springer, New Delhi.Horvath, 2014.
[10]R. Kaur and I. Singh, “Image Retrieval System Using Improved Local Binary Patterns and GLCM Matrices,” Imperial Journal of Interdisciplinary Research, 3(6), 2017.
[11]Ahonen, T., Hadid, A. and Pietikainen, M., 2006. Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence, (12), pp.2037-2041.
[12]P. Liu, J. M. Guo, K. Chamnongthai, and H. Prasetyo, “Fusion of color histogram and LBP-based features for texture image retrieval and classification,” Information Sciences, 390, pp.95-111, 2017.
[13]C. Singh, E. Walia, and K. P. Kaur, “Color texture description with novel local binary patterns for effective image retrieval,” Pattern recognition, 76, pp.50-68, 2018.
[14]J. Z. Wang, Li,J. and G. Wiederhold, “SIMPLIcity: Semantics-sensitive integrated matching for picture libraries,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(9), pp. 947-963, 2001.
[15]M. Petrou and C. Petrou, “Image processing: the fundamentals,” Book, John Wiley \& Sons, 2010.
[16]R. Hall, “Illumination and color in computer generated imagery,” Springer Science & Business Media, 2012.
[17]H. Al-Jubouri, “Multi evidence fusion scheme for content-based image retrieval by clustering localised colour and texture features,” Doctoral thesis, University of Buckingham, 2015.
[18]A. Eleyan and H. Demirel, “Co-occurrence matrix and its statistical features as a new approach for face recognition,” Turk J Elec Eng \& Comp Sci, 19(1), pp. 97-107, 2011.
[19]E. A. Fox and J., A. Shaw, “Combination of Multiple Searches,” NIST SPECIAL PUBLICATION SP, pp. 243-246, 1994.
[20]J. Lee, “Analyses of Multiple Evidence Combination,” New York, NY, USA, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 267-276, 1997.
[21]L. Feng, J. Wu, S. Liu and H. Zhang, “Global correlation descriptor: a novel image representation for image retrieval,” Journal of Visual Communication and Image Representation, Vol.33, pp.104-114, 2015.
[22]T. Ojala, M. Pietikainen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” Proceedings of the 12th International Conference on Pattern Recognition Computer Vision \& Image Processing, pp. 582-585, 1994.
[23]T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7), pp. 971-987, 2002.
[24]A. Field, "Discovery statistics using SPSS", London, UK: SAGE Publications, 2006.
[25]M. K. Kundu, M. Chowdhury, and S.R. Bulò, “A graph-based relevance feedback mechanism in content-based image retrieval”, Knowledge-Based Systems, Vol.73, pp.254-264, 2015.
[26]L. Feng, J. Wu, S. Liu and H. Zhang. “Global correlation descriptor: a novel image representation for image retrieval”, Journal of Visual Communication and Image Representation, Vol.33, pp.104-114, 2015.