Content Based Image Retrieval Using Extended Local Tetra Patterns

Authors

  • Sipani A Department of Computer Science and Engineering, National Institute of Technology, Warangal, India
  • Krishna P Department of Computer Science and Engineering, National Institute of Technology, Warangal, India
  • Chandra S Department of Computer Science and Engineering, National Institute of Technology, Warangal, India

Keywords:

Content Based Image Retrieval (CBIR), Local Tetra Patterns (LTrP), Gabor Filters, Histogram Equalization, Moment Invariants

Abstract

In this modern world, finding the desired image from huge databases has been a vital problem. Content Based Image Retrieval is an efficient method to do this. Many texture based CBIR methods have been proposed so far for better and efficient image retrieval. We aim to give a better image retrieval method by extending the Local Tetra Patterns (LTrP) for CBIR using texture classification by using additional features like Moment Invariants and Color moments. These features give additional information about the color and rotational invariance. So an improvement in the efficiency of image retrieval using CBIR is expected.

References

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Subrahmanyam Murala, R. P. Maheshwari and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval” in IEEE Transactions on Image Processing, Vol. 21, No. 5, pp 2874 - 2886 May. 2012.

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Published

2014-12-06

How to Cite

[1]
A. Sipani, P. Krishna, and S. Chandra, “Content Based Image Retrieval Using Extended Local Tetra Patterns”, Int. J. Comp. Sci. Eng., vol. 2, no. 11, pp. 11–17, Dec. 2014.

Issue

Vol. 2 No. 11 (2014): IJCSE November Edition

Section

Research Article

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