Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System

Document Type : Research Paper

Authors

1 Ph.D Research Scholar, Computer Engineering, Madhav University, Rajasthan, India.

2 Research Supervisor, Computer Engineering, Madhav University, Rajasthan, India.

3 Head of Department, Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India.

10.22059/jitm.2021.80765

Abstract

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.

Keywords


Budanitsky, A., &Hirst, G. (2001, June). Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on WordNet and other lexical resources (Vol. 2, pp. 2-2).
Cao, X., & Wang, S. (2012). Research about image mining technique. In Communications and Information Processing (pp. 127-134). Springer, Berlin, Heidelberg.
El-Alami, M. E. (2014). A new matching strategy for content based image retrieval system. Applied Soft Computing, 14, 407-418.
Franzoni, V., Leung, C. H., Li, Y., Mengoni, P., &Milani, A. (2015, June). Set similarity measures for images based on collective knowledge. In International Conference on Computational Science and Its Applications (pp. 408-417). Springer, Cham.
Finlayson, M. (2014, January). Java libraries for accessing the princetonwordnet: Comparison and evaluation. In Proceedings of the Seventh Global Wordnet Conference (pp. 78-85).
Franzoni, V., Milani, A., Pallottelli, S., Leung, C. H., & Li, Y. (2015, August). Context-based image semantic similarity. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 1280-1284). IEEE.
G. Mont´ufar, R. Pascanu, K. Cho, and Y. Bengio, (2014), On the number of linear regions of deep neural networks. In NIPS.
Girshick., R. (2015), Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448.
Goel, N., &Sehgal, P. (2013, August). Weighted semantic fusion of text and content for image retrieval. In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 681-687). IEEE.
Goel, N., & Sehgal, P. (2014). Image Retrieval Using Fuzzy Color Histogram and Fuzzy String Matching: A Correlation-Based Scheme to Reduce the Semantic Gap. In Intelligent Computing, Networking, and Informatics (pp. 327-341). Springer, New Delhi.
He., K and Sun., J. 2015 Convolutional neural networks at constrained time cost. In CVPR.
Huang, Z. C., Chan, P. P., Ng, W. W., & Yeung, D. S. (2010, July). Content-based image retrieval using color moment and Gabor texture feature. In 2010 International conference on machine learning and cybernetics (Vol. 2, pp. 719-724). IEEE.
Li, X., Uricchio, T., Ballan, L., Bertini, M., Snoek, C. G., & Bimbo, A. D. (2016). Socializing the semantic gap: A comparative survey on image tag assignment, refinement, and retrieval. ACM Computing Surveys (CSUR), 49(1), 1-39.
Lin, C. H., Chen, R. T., & Chan, Y. K. (2009). A smart content-based image retrieval system based on color and texture feature. Image and Vision Computing, 27(6), 658-665.
M. D. Zeiler and R. Fergus (2014). Visualizing and understanding convolutional neural networks. In ECCV.
MATLAB and Statistics Toolbox Release 2013a, TheMathWorks, Inc., Natic, Massachusetts, United States.
Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41.
Murala, S., Gonde, A. B., &Maheshwari, R. P. (2009, March). Color and texture features for image indexing and retrieval. In 2009 IEEE International Advance Computing Conference (pp. 1411-1416). IEEE.
Neelima, N., & Reddy, E. S. (2016). An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 257-265). Springer, Cham.
Oliva, A., &Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision, 42(3), 145-175.
R. K. Srivastava, K. Greff, and J. Schmidhuber (2015), Training very deep networks. 1507.06228.
Singh, K., Singh, K. J., & Kapoor, D. S. (2014, September). Image Retrieval for Medical Imaging Using Combined Feature Fuzzy Approach. In 2014 International Conference on Devices, Circuits and Communications (ICDCCom) (pp. 1-5). IEEE.
Wu, L., Hua, X. S., Yu, N., Ma, W. Y., & Li, S. (2011). Flickr distance: a relationship measure for visual concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(5), 863-875.
Zarchi, M. S., Monadjemi, A., &Jamshidi, K. (2015). A concept-based model for image retrieval systems. Computers & Electrical Engineering, 46, 303-313.