A Study on Design and Development of Framework for Content-based Image Retrieval

Abstract

CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.


How to cite this article:
Fatima S. A Study on Design and Development of Framework for Content based Image Retrieval. J Adv Res Electro Engi Tech 2021; 8(3&4): 12-15.


DOI: https://doi.org/10.24321/2456.1428.202103

References

1. Zhang B, Tomai CI, Zhang A et al. An Adaptive Image Retrieval System using Wavelets.
2. Antani, Sameer K, Rodney L et al. Content-based image retrieval for large biomedical image archives. Medinfo 2004.
3. Ramos, José. Content-based image retrieval by metric learning from radiology reports: Application to interstitial lung diseases. IEEE journal of biomedical and health informatics 2016; 20(1): 281-292.
4. Varish, Naushad, Pal AK. Content based image retrievalusing statistical features of color histogram. Signal
Processing, Communication and Networking (ICSCN), 3rd International Conference. IEEE, 2015.
5. Shinde, Sandhya R, Sabale S et al. Experiments on content based image classification using Color feature extraction. In Communication, Information and Computing Technology (ICCICT) International Conference. 2015; 1-6.
6. Patil, Kumar J, Kumar R. Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food 2016.
7. Katta S, Pabboju S, Babu AV. A CBIR classification using support vector machines. International Conference on Advances in Human Machine Interaction (HMI), 2016.
8. Kaur N, Jindal S, Kaur B. Relevance Feedback Based CBIR System Using SVM and Bayes Classifier. Computational Intelligence & Communication Technology (CICT), Second International Conference. IEEE, 2016.
9. Deole P, Ashok. Content-based image retrieval using color feature extraction with KNN classification. IJCSMC 2014; 3(5): 1274-80.
10. Sundaresan SM, Srinivasagan DK. Design of Image Retrieval Efficacy System Based on CBIR. Int J of Advanced Research in Computer Science and Software Engineering 2013; 3(4): 48-53.
11. Rani D, Goyal M. A Research Paper on Content-based Image Retrieval System using Improved SVM Technique. International Journal of Engineering and Computer Science (IJECS) 2014; 3(12): 9755-7760.
12. Singh S, Mangijao, Hemachandran K. Contentbased image retrieval based on the integration of color histogram, color moment and Gabor texture. International Journal of Computer Applications 2012; 59: 17.
13. Katare A, Mitra SK, Banerjee A. Content-based Image Retrieval System for Multi Object Images Using Combined Features. International Conference on Computing: Theory and Applications.
14. Vimina ER. CBIR using local and global properties of image sub-blocks. Int J Adv Sci Technology 2012; 48: 11-22.
15. Gang H. Content-Based Image Retrieval using Texture Structure Histogram.
16. Carson C, Thomas M, Belongie S et al. Blobworld: A System for Region-Based Image Indexing and Retrieval. International Conference on Visual Information. Systems. Springer-Verlag. Available: http://digitalassets.lib.berkeley.edu/techreports/ucb/text/CSD-99-1041
17. Li Y. Object and Concept Recognition for Content-Based Image Retrieval.
18. Vimina ER, Jacob KP. Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest. Journal of Image and Graphics 2013; 1(1): 7-17.
19. Hussain A. Comparative study on content-based image retrieval (CBIR). Advanced Computer Science Applications and Technologies, International Conference, 2012.
20. Sukhada A. Content Based Image Retrieval in Biomedical Images Using SVM Classification with Relevance Feedback. International Journal of Scientific and Research Publications 2002; 3(11): 1-7.
21. José R. Content-based image retrieval by metric learning from radiology reports: Application to interstitial lung diseases. IEEE journal of biomedical and health informatics 2016; 20(1): 281-292.
Published
2022-02-17
How to Cite
FATIMA, Shaheen. A Study on Design and Development of Framework for Content-based Image Retrieval. Journal of Advanced Research in Electronics Engineering and Technology, [S.l.], v. 8, n. 3&4, p. 12-15, feb. 2022. ISSN 2456-1428. Available at: <https://thejournalshouse.com/index.php/electronics-engg-technology-adr/article/view/539>. Date accessed: 17 aug. 2022.