Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Content Based Image Retrieval Scheme using Color, Texture and Shape Features Along Edge Detection

Print
PDF
IJCA Proceedings on International Conference on Advances in Emerging Technology
© 2018 by IJCA Journal
ICAET 2017 - Number 1
Year of Publication: 2018
Authors:
Sunpreet Kaur
Sonika Jindal

Sunpreet Kaur and Sonika Jindal. Article: Content Based Image Retrieval Scheme using Color, Texture and Shape Features Along Edge Detection. IJCA Proceedings on International Conference on Advancements in Engineering and Technology ICAET 2017(1):1-6, July 2018. Full text available. BibTeX

@article{key:article,
	author = {Sunpreet Kaur and Sonika Jindal},
	title = {Article: Content Based Image Retrieval Scheme using Color, Texture and Shape Features Along Edge Detection},
	journal = {IJCA Proceedings on International Conference on Advancements in Engineering and Technology},
	year = {2018},
	volume = {ICAET 2017},
	number = {1},
	pages = {1-6},
	month = {July},
	note = {Full text available}
}

Abstract

Content Based Image Retrieval is a very dominant area which uses the perceptible contents of the image such as color, texture and shape combines to represent the features of the image which is discussed in this paper . Operative research in CBIR is engaged towards the advancement of different methodologies for analyzing, explaining, cataloging and indexing the heavy databases. The proposed scheme is based on three algorithms: Color distribution entropy(CDE),Color level co-occurrence(CLCM) and Canny edge detection+hue moments. CDE considers the correlativity of the color spatial distribution of an image or we can say effectively tells the spatial color information of images . CLCM takes in account the texture features of an image, whose base is from the old algorithm grey level co-occurrence matrix(GLCM) which only takes the grey level images but in CLCM it takes colored texture images ,it is a colored alternative to old texture recognizing GLCM. And Canny edge detector is used for detecting the edge of an image with hue moments which are frequently used as shape extraction feature considering its qualities of in variance under translation, changes in scale and rotation. The proposed scheme achieves a higher retrieval result by taking these diverse and primitive image descriptors which relates to better retrieval result. The similarity measure matrix is both Euclidean and Manhattan distance.

References

  • J. L. R. Datta and J. Z. Wang, "Image retrieval: Ideas, influences, and trends of the new age [j]," vol. vol. 40, no. no. 2. ACM Computing Surveys, 2008, pp. pp. 1–60.
  • Y. Y. X. Wang and H. Yang, "An effective image retrieval scheme using color, texture and shape feature. " Computer Standards & Interfaces, 2011, pp. pp. 59–68.
  • H. S. X. J. Zhijie Zhao, Qin Tian and J. Guo, "Content based image retrieval scheme using color, texture and shape features," vol. Vol. 9, no. No. 1. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016, pp. pp. 203–212.
  • F. Alamdar and M. R. Keyvanpour, "A new color feature extraction methods based on dynamic color distribution of neighborhoods," vol. vol. 8 Iss. 5, no. no. 1. IJCSI International Journal of Computer Science Issues, 2013, pp. pp. 42–48.
  • H. B. Kekre and K. Sonawane, "Use of equalized histogram cg on statistical parameters in bins approach for cbir. " IEEE International Conference on Advances in Technology and Engineering (ICATE), 2013, pp. pp. 1–6.
  • C. Yang and X. Gu, "Combining pcnn with color distribution entropy and vector gradient in feature extraction. " The 8th IEEE International Conference on Natural Computation (ICNC), 2012, pp. pp. 207–211.
  • M. G. V. S. K. V. Shriram, Dr. P. L. K Priadarsini and R. A. Sivaraman, "Nnovel approach using t. h. e. s methodology for cbir. " IEEE International Conference on Signal Processing and Pattern Recognition (ICSIPR), 2013, pp. pp. 10–13.
  • N. G. W. Khan, S. Kumar and N. Khan, "A proposed method for image retrieval using histogram values and texture descriptor analysis," vol. vol. 1, Iss. 2. International Journal of Soft Computing and Engineering (IJSCE) ISSN:2231-2307, 2011.
  • Y. Y. X. Wang and H. Yang, "An effective image retrieval scheme using color, texture and shape feature. " Computer Standards & Interfaces, 2011, pp. pp. 59–68.
  • V. k. b. Gurpreet kaur, "Improved color edge detection by fusion of hue, pca & hybrid canny," vol. vol. 2, no. no. 1. International Journal of Science, Engineering and Technologies (IJSET), June. 2015.
  • L. Wang and L. Yan, "Edge detection of color image using vector mor-phological operators. " In Computer Science and Network Technology (ICCSNT), 2nd International Conference on, IEEE, 2012, pp. pp. 2211– 2215.
  • L. M. Hao, Geng and H. Feng, "Improved selfadaptive edge detection method based on canny. " In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, vol. 2 . IEEE, 2013, pp. pp. 527–530.
  • H. H. Ehsan Nadernejad, Sara Sharifzadeh, "Edge detection techniques: Evaluations and comparisons," research gate, 2008.