Main Article Content
Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace.
Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time.
Main Findings: This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query.
Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace.
Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.
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- Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011), Content-based image retrieval in radiology: current status and future directions. Journal of Digital Imaging, 24(2), 208-222. https://doi.org/10.1007/s10278-010-9290-9
- Amanatiadis, A., Kaburlasos, V. G., Gasteratos, A., & Papadakis, S. E. (2011), Evaluation of shape descriptors for shape-based image retrieval. IET Image Processing,5(5), 493-499. https://doi.org/10.1049/iet-ipr.2009.0246
- Belongie, S., Carson, C., Greenspan, H., & Malik, J. (1998), Color-and texture based image segmentation using EM and its application to content-based image retrieval. IEEE 6th International Conference on Computer Vision, 675-682. https://doi.org/10.1109/ICCV.1998.710790
- Dalia,R. & Gupta, R. (2019), Comparison of Image Compression Methods for Image Transmission Over Wireless Sensor Network. Journal of Computational and Theoretical Nanoscience, 16, 3912-3916. https://doi.org/10.1166/jctn.2019.8270
- Di Sciascio, E., Mingolla, G., & Mongiello, M. (1999), Content-based image retrieval over the web using query by sketch and relevance feedback. Visual Information and Information Systems, Springer Berlin Heidelberg. 123-130. https://doi.org/10.1007/3-540-48762-X_16
- Komali, A., Kumar, V. S., Babu, K. G., & Ratnam, A. S. K (2012). 3D color feature extraction in content-based image retrieval. International Journal of Soft Computing and Engineering, 2(3), 560-563.
- Kumar, G., Singh, G., Bhatanagar, V., & Jyoti, K. (2019). Scary Dark Side of Artificial Intelligence: A Perilous Contrivance to Mankind. Humanities & Social Sciences Reviews, 7(5), 1097-1103. https://doi.org/10.18510/hssr.2019.75146
- Lu, Z. M., Li, S., & Burkhardt, H. (2006), A content-based image retrieval scheme in JPEG compressed domain International Journal of Innovative Computing, Information and Control, 2(4), 831-839.
- Manocha, N. & Gupta, R. (2019), A Comparative Analysis of Existing Satellite Image Enhancement Techniques for Effective Visual Display, Journal of Computational and Theoretical Nanoscience, 16, 4003-4007. https://doi.org/10.1166/jctn.2019.8285
- Sasikala, S., & Gandhi, R. S. (2015), Efficient Content Based Image Retrieval System with Metadata Processing. International Journal for Innovative Research in Science and Technology, 1(10), 72-77.
- Singh, V. & Gupta, R (2016), Semantic Based Image Retrieval from Cyberspace: A Review Study, International Jouurnal of Advanced Research in Computer Science. 7(4), 16-21.
- Wang, X., Qiu, S., Liu, K., & Tang, X. (2014). Web Image Re-Ranking Using Query-Specific Semantic Signature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(4), 810-823. https://doi.org/10.1109/TPAMI.2013.214
- Yu, J., Rui, Y., & Tao, D. (2014), Click prediction for web image re-ranking using multimodal sparse coding. IEEE Transactions on Image Processing, 23(5), 2019-2032. https://doi.org/10.1109/TIP.2014.2311377