Open Access   Article Go Back

Content-Based Image Retrieval Using Extended Local Tetra Patterns

Deepika Choudhary1 , Amit 2 , Suraj Malik3 , Pankaj Pratap Singh4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 91-96, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.9196

Online published on Feb 28, 2019

Copyright © Deepika Choudhary, Amit, Suraj Malik, Pankaj Pratap Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Deepika Choudhary, Amit, Suraj Malik, Pankaj Pratap Singh, “Content-Based Image Retrieval Using Extended Local Tetra Patterns,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.91-96, 2019.

MLA Style Citation: Deepika Choudhary, Amit, Suraj Malik, Pankaj Pratap Singh "Content-Based Image Retrieval Using Extended Local Tetra Patterns." International Journal of Computer Sciences and Engineering 7.2 (2019): 91-96.

APA Style Citation: Deepika Choudhary, Amit, Suraj Malik, Pankaj Pratap Singh, (2019). Content-Based Image Retrieval Using Extended Local Tetra Patterns. International Journal of Computer Sciences and Engineering, 7(2), 91-96.

BibTex Style Citation:
@article{Choudhary_2019,
author = {Deepika Choudhary, Amit, Suraj Malik, Pankaj Pratap Singh},
title = {Content-Based Image Retrieval Using Extended Local Tetra Patterns},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {91-96},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3624},
doi = {https://doi.org/10.26438/ijcse/v7i2.9196}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.9196}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3624
TI - Content-Based Image Retrieval Using Extended Local Tetra Patterns
T2 - International Journal of Computer Sciences and Engineering
AU - Deepika Choudhary, Amit, Suraj Malik, Pankaj Pratap Singh
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 91-96
IS - 2
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
585 282 downloads 185 downloads
  
  
           

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 textures 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 extended version of LTrP. These features give additional information about the color and rotational invariance. So an improvement in the efficiency of image retrieval using CBIR is expected.

Key-Words / Index Term

Local binary Patterns, Content Based Image Retrieval (CBIR), Local Ternary Patterns (LTP), Local Tetra Patterns (LTrP), Extended Local Tetra Pattern (ELTrP), Histogram Equalization

References

[1] E. Hayman, B. Caputo, M. Fritz, and J.-O. Eklundh, “On the significance of real-world conditions for material classification," in European conference on computer vision. Springer, 2004, pp. 253-266.
[2] J. Sivic and A. Zisserman, “Video google: A text retrieval approach to object matching in videos," in Ninth IEEE International Conference on Computer Vision (ICCV 2003) 2003, p. 1470.
[3] H. Jegou and O. Chum, “Negative evidences and co-occurrences in image retrieval: The benefit of pca and whitening," in European conference on Computer Vision-ECCV 2012. Springer, 2012, pp. 774-787.
[4] Y. Rui, T. S. Huang, and S.-F. Chang, “Image retrieval: Current techniques, promising directions, and open issues," Journal of visual communication and image representation, vol. 10, no. 1, pp. 39-62, 1999.
[5] A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content based image retrieval at the end of the early years," IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 12, pp. 1349-1380, 2000.
[6] O. Marques and B. Furht, “Content-based image and video retrieval. Springer Science & Business Media, 2002, vol. 21.
[7] A. Diaz, “Through the google goggles: Sociopolitical bias in search engine design," in Web search. Springer, 2008, pp. 11- 34.
[8] A. J. Afifi and W. M. Ashour, “Image retrieval based on content using color feature," International Scholarly Research Notices, vol. 2012, 2012.
[9] S. Banerji, A. Verma, and C. Liu, “Novel color lbp descriptors for scene and image texture classification," in Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV). Citeseer, 2011, p. 1.
[10] J. R. Smith and S.-F. Chang, “Tools and techniques for color image retrieval," in Storage and Retrieval for Still Image and Video Databases IV, vol. 2670 International Society for Optics and Photonics, 1996, pp. 426-438.
[11] R. Polana and R. Nelson, “Temporal texture and activity recognition" in Motion-based recognition. Springer, 1997, pp. 87-124.
[12] R. C. Nelson and R. Polana, “Qualitative recognition of motion using temporal texture," CVGIP: Image understanding, vol. 56, no. 1, pp. 78-89, 1992.
[13] T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, no. 1, pp. 51-59, 1996.
[14] T. Leung and J. Malik, Representing and recognizing the visual appearance of materials using three-dimensional textons," International journal of computer vision, vol. 43, no. 1, pp. 29-44, 2001.
[15] A. K. Jain and F. Farrokhnia, “Unsupervised texture segmentation using gabor filters," Pattern recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[16] S. Murala, R. 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.
[17] B. Zhang, Y.Gao “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.
[18] Priyanka R R, Mahesh M, Pallavi S S, Jayapala G and Pooja M R “Crop Protection by an alert Based System using Deep Learning Concept,” International Journal of Scientific Research in Computer Science and Engineering, vol 6, no 6, 2018.
[19] R. Zade, N. Khadgi, M. Kasbe and T. Mujawar “Online Garbage Monitoring System Using Arduino and LabVIEW,” International Journal of Scientific Research in Computer Science and Engineering, vol 6, no 6, 2018.