Texture and Color Features based Color Image Retrieval using Canonical Correlation

1
K. Seetharaman
K. Seetharaman
2
Bachala Shyam Kumar
Bachala Shyam Kumar
1 Pondicherry College of Engineering

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Texture and Color Features based Color Image Retrieval using Canonical Correlation Banner
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This paper proposes a novel technique, based on Canonical correlation analysis, and the Chi-square test is employed to test the significance of the correlation coefficients. If it is significant, then it is concluded that the input query and target images are same or similar; otherwise, it is inferred that the two images are significantly different. In order to experiment the proposed canonical correlation method, a database is designed and constructed with the help of different types of images and their feature vectors. The F β -measure is applied to evaluate the performance of the proposed method. The obtained results expose that the proposed technique yields better results than the existing.

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References

  1. A Amanatiadis,V Kaburlasos,A Gasteratos,S Papadakis (2011). Evaluation of shape descriptors for shape-based image retrieval.
  2. S Belongie,C Carson,H Greenspan,J Malik (2002). Recognition of images in large databases using colour and texture.
  3. David Hardoon,John Shawe-Taylor (2003). Kcca for different level precision in content-based image retrieval.
  4. Y Deng,B Manjunath (1999). An efficient lowdimensional colour indexing scheme for regionbased image retrieval.
  5. C-S Fuh,S-W Cho,K Essig (2000). Hierarchical colour image region segmentation for contentbased image retrieval system.
  6. C Fyfe,P Lai (2001). Kernel and nonlinear canonical correlation analysis.
  7. T Ho,J Hull,S Srihari (1994). Decision combination in multiple classifier systems.
  8. S Hoi,W Liu,S Chang (2010). Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval and Clustering.
  9. I-S Hsieh,H Fan (2001). Multiple classifiers for colour flag and trademark image retrieval.
  10. J-W Hsieh,W Eric,L Grimson (2003). Spatial template extraction for image retrieval by region matching.
  11. J Huang,S Kunar,M Mitra,W Zhu,R Zabih (1997). Image indexing using colour correlogram.
  12. F Jing,M Li,H-J Zhang,B Zhang (2004). An Efficient and Effective Region-Based Image Retrieval Framework.
  13. F Jing,B Zhang,F Lin,Y Ma,H Zhang (2001). A novel region-based image retrieval method using relevance feedback.
  14. F Kimura,M Shridhar (1991). Handwritten numerical recognition based on multiple algorithm.
  15. R Krishnamoorthi,S Sathiya Devi (2013). A simple computational model for image retrieval with weighted multifeatures based on orthogonal polynomials and genetic algorithm.
  16. G Liu,Z-Y Lib,L Zhangc,Y Xud (2011). Image retrieval based on micro-structure descriptor.
  17. Subrahmanyam Murala,R Maheshwari,R Balasubramanian (2012). Directional local extrema patterns: a new descriptor for content based image retrieval.
  18. Alexander Pentland,Rosalind Picard,S Scarloff (1994). <title>Photobook: tools for content-based manipulation of image databases</title>.
  19. K Seetharaman (2015). Image retrieval based on micro-level spatial structure features and content analysis using Full Range Gaussian Markov Random Field model.
  20. K Seetharaman,M Kamarasan (2014). Statistical framework for image retrieval based on multiresolution features and similarity method.
  21. K Seetharaman,M Jaikarthic (2014). Statistical Distributional Approach for Scale and Rotation Invariant Colour Image Retrieval Using Multivariate Parametric tests and Orthogonality Condition.
  22. J Smith,C Li (1999). Image classification and querying using composite region templates.
  23. Ch Srinivasa Rao,S Srinivas Kumar,B Chatterji (2007). Content based image retrieval using Contourlet Transform.
  24. Markus Stricker,Markus Orengo (1995). Similarity of color images.
  25. W Sun,L Chen (1994). Multivariate Statistical Analysis.
  26. C Van Rijsbergen (1979). Information Retrieval.
  27. James Wang,Yanping Du (2001). Scalable integrated region-based image retrieval using IRM and statistical clustering.
  28. Yi Yang,Shawn Newsam (2013). Geographic Image Retrieval Using Local Invariant Features.
  29. K-H Yap,K Wu (2005). A soft relevance framework in content-based image retrieval systems.
  30. X Zhang,K Fang (1999). Multivariate Statistical Introduction.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

K. Seetharaman. 2016. \u201cTexture and Color Features based Color Image Retrieval using Canonical Correlation\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 15 (GJRE Volume 15 Issue J6): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-J Classification: FOR Code: 080106
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v1.2

Issue date

January 17, 2016

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English

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This paper proposes a novel technique, based on Canonical correlation analysis, and the Chi-square test is employed to test the significance of the correlation coefficients. If it is significant, then it is concluded that the input query and target images are same or similar; otherwise, it is inferred that the two images are significantly different. In order to experiment the proposed canonical correlation method, a database is designed and constructed with the help of different types of images and their feature vectors. The F β -measure is applied to evaluate the performance of the proposed method. The obtained results expose that the proposed technique yields better results than the existing.

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Texture and Color Features based Color Image Retrieval using Canonical Correlation

K. Seetharaman
K. Seetharaman
Bachala Shyam Kumar
Bachala Shyam Kumar

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