Open Access   Article

Hybrid Features For Content Based Image Retrieval System

A.D. Mahajan1 , S. Chaudhary2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-10 , Page no. 11-15, Oct-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i10.1115

Online published on Oct 31, 2018

Copyright © A.D. Mahajan, S. Chaudhary . 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.

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Citation

IEEE Style Citation: A.D. Mahajan, S. Chaudhary, “Hybrid Features For Content Based Image Retrieval System”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.11-15, 2018.

MLA Style Citation: A.D. Mahajan, S. Chaudhary "Hybrid Features For Content Based Image Retrieval System." International Journal of Computer Sciences and Engineering 6.10 (2018): 11-15.

APA Style Citation: A.D. Mahajan, S. Chaudhary, (2018). Hybrid Features For Content Based Image Retrieval System. International Journal of Computer Sciences and Engineering, 6(10), 11-15.

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Abstract

The “speedy progress in multimedia and imaging technology, the numbers of images uploaded and shared on the internet have increased. It leads to develop the highly effective image retrieval system to satisfy the human needs. The content-Based image retrieval (CBIR) system which retrieves the image based on the Low level features such as color, texture and shape which are not sufficient to describe the user’s high level perception for images. Therefore reducing this semantic gap problem of image retrieval is challenging task. Some of the most important notions in image retrieval are keywords, terms or concepts. Terms are used by humans to describe their information need and it also used by system as a way to represent images. Here in this paper different types of features their advantage and disadvantages are described. We have carried out comparative analysis of different techniques used in our system to determine best suitable technique to be used for our proposed system. We have analyze the our proposed system on large image dataset and our approach gives high precision and required less computations which proves efficiency of our system. In our proposed system we have evaluated the performance of our feature extraction techniques i.e. FCH and GWT using precision and recall metric and compared the result with existing feature extraction approaches i.e. color moment and GWT. Implementation results show that the feature extraction techniques for the proposed system are better than the existing techniques. SVM Classifier also gives good accuracy using these feature extraction” techniques.

Key-Words / Index Term

CBIR, Color Moment, Fuzzy Color Histrogram, Gabor Wavelate, Support Vector Machine

References

[1] ElAlami, M.E."A new matching strategy for content based image retrieval system." Applied Soft Computing 14 (2014): 407-418.
[2] Murala, Subrahmanyam, Anil Balaji Gonde, and Rudra Prakash Maheshwari. "Color and texture features for image indexing and retrieval." In Advance Computing Conference, 2009. IACC 2009. IEEE International, pp. 1411-1416. IEEE, 2009.
[3] Zhang, Dengsheng, Aylwin Wong, Maria Indrawan, and Guojun Lu. "Content-based image retrieval using Gabor texture features." In IEEE Pacific-Rim Conference on Multimedia, University of Sydney, Australia. 2000.
[4] Howarth, Peter, and Stefan Rüger. "Evaluation of texture features for content-based image retrieval." In Image and Video Retrieval, pp. 326-334. Springer Berlin Heidelberg, 2004.
[5] Lin, Chuen-Horng, Rong-Tai Chen, and Yung-Kuan Chan. "A smart content-based image retrieval system based on color and texture feature." Image and Vision Computing 27, no. 6 (2009): 658-665.
[6] Saad, Michele. "Low-level color and texture feature extraction for content-based image retrieval." Final Project Report, EE K 381 (2008): 20-28.
[7] Jhanwar, N., Subhasis Chaudhuri, Guna Seetharaman, and Bertrand Zavidovique. "Content based image retrieval using motif cooccurrence matrix."Image and Vision Computing 22, no. 14 (2004): 1211-1220.
[8] Yue, Jun, Zhenbo Li, Lu Liu, and Zetian Fu. "Content-based image retrieval using color and texture fused features." Mathematical and Computer Modelling54, no.3 (2011): 1121-1127.
[9] Xianzhe Cao and Shimin Wang, “Research about Image Mining Technique,” in proc. Springer ICCIP,2012, pp.127-134.
[10] Ahmad Alzu’bi, Abbes Amira and Naeem Ramzan, “Semantic content-based image retrieval: A comprehensive study,” in Elseveir Journal of Visual Communication and Image Representation, Vol. 32, pp. 20-54 ,2015
[11] V. Franzoni, A. Milani, S. Pallottelli, C. H. C. Leung and Yuanxi Li, "Context-based image semantic similarity," in proc. IEEE twelveth international conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015, pp. 1280-1284.
[12] Valentina Franzoni, Clement H.C. Leung, Yuanxi Li,Paolo Mengoni and Alfredo Milani,” Set Similarity Measures for Images Based on Collective Knowledge,” in proc. Springer ICCSA,2015,pp.408-417
[13] Elalami,“A New Matching Strategy for Content Based Image Retrieval System,” in ACM Appl. Soft Comput., vol. 14,pp. 407-418,2014
[14] Mohsen Sardari Zarchi, Amirhasan Monadjemi and Kamal Jamshidi, ” A concept- based model for image retrieval systems,” in Elsevier Computers & Electrical Engineering, vol. 46 , pp. 303-313, 2015
[15] N. Goel and P. Sehgal, "Weighted semantic fusion of text and content for image retrieval," in proc. IEEE International Conference Advances in Computing, Communications and Informatics (ICACCI), 2013 , pp. 681-687.
[16] K. Singh, K. J. Singh and D. S. Kapoor, "Image Retrieval for Medical Imaging Using Combined Feature Fuzzy Approach," in proc. IEEE International Conference on Devices, Circuits and Communications (ICDCCom), 2014, pp. 1-5
[17] N. Goel and P. Sehgal,” Image Retrieval Using Fuzzy Color Histogram and Fuzzy String Matching: A Correlation-Based Scheme to Reduce the Semantic Gap", in proc. Springer Intelligent Computing, Networking, and Informatics,2014, pp. 327- 341.
[18] C.-H. Lin, R.-T. Chen and Y.-K. Chan, “A smart content-based image retrieval system based on color and texture feature”, in Elsevier Image and Vision Computing, Vol. 27 , pp. 658–665,2009.
[19] Zhi-chun huang, Patrick P. K. Chan, Wing W. Y. Ng, D aniel s. Yeung" Content- based image retrieval using color moment and Gabor texture feature", Proc. IEEE Ninth international Conference on Machine Learning and Cybernetics, 2010, pp 719-724.
[20] Nizampatnam Neelima and E. Sreenivasa Reddy, “An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM”, in proc. Springer Advances in Intelligent Systems and Computing, Vol. 425,2015, pp 257-265.
[21] Subrahmanyam Murala, Anil Balaji Gonde and R.P. Maheshwari, “Color and Texture Features for Image Indexing and Retrieval”, in proc. IEEE international advanced computing conference , 2009, pp. 1411-1416.
[22] L. Wu, X. Hua, N. Yu, W. Ma, and S. Li, ‘‘Flickr distance: A relationship measure for visual concepts,’’ in IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, pp. 863– 875, 2012.
[23] L. Wu, X.-S. Hua, N. Yu, W.-Y. Ma, and S. Li, “Flickr Distance,” in Proc. 16th ACM International Conf. Multimedia, 2008, pp. 31-40
[24] G. A. Miller, “Wordnet: a lexical database for english”, in ACM Communications of the, 38(11):39–41, 1995.
[25] A. Budanitsky and G. Hirst, “Semantic Distance in Wordnet: An Experimental, Application-Oriented Evaluation of Five Measures,” Proc. WordNet and Other Lexical Resources, 2001
[26] S. S. Hiwale, D. Dhotre and G. R. Bamnote, "Quick interactive image search in huge databases using Content-Based image retrieval," in proc. IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, pp. 1-5.
[27] K. Konstantinidis, A. Gasteratos, I. Andreadis, “Image Retrieval Based on Fuzzy Color Histogram Processing”, in Elsevier Optics Communications, Vol. 248, pp. 375-386, 2005.
[28] J. Liu, Z. Li, J. Tang, Y. Jiang and H. Lu, "Personalized Geo-Specific Tag Recommendation for Photos on Social Websites," in IEEE Transactions on Multimedia, vol. 16, pp. 588-600, 2014.
[29] Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. Journal of Computer Vision., 42, 2001.
[30] X. Li, T. Uricchio, L. Ballan, M. Bertini, C. Snoek, and A. Del Bimbo, “Socializing the semantic gap: A comparative survey on image tag assignment, refinement and retrieval,” in ACM Computing Surveys, 2016, in press.
[31] Cinemast,”Semantic Gap in image analysis”,
[32] Internet: https://en.wikipedia.org/wiki/Semantic_gap, Apr. 2016.
[33] Dong-Chul Park, “Image Classification Using Naïve Bayes Classifier”, in International Journal of Computer Science and Electronics Engineering, vol. 4 , pp. 135-139, 2016
[34] MATLAB and Statistics Toolbox Release 2013a, The MathWorks, Inc., Natick, Massachusetts, United States
[35] Finlayson and Mark Alan “Java Libraries for Accessing the Princeton Wordnet: Comparison and Evaluation” in Proceedings of the 7th International Global WordNet Conference, 2014, pp. 78-85 .
[36] Ted Pedersen, WordNet::Similarity. [Online]
[37] Available at: http://wn- similarity.sourceforge.net/ [Accessed on 15 may 2017].