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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.8, No.3, Mar. 2016

An Interactive Approach for Retrieval of Semantically Significant Images

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Author(s)

Pranoti P. Mane, Amruta B. Rathi, Narendra G. Bawane

Index Terms

Semantic gap;content-based image retrieval;relevance feedback;HSV histogram;local binary pattern;color coherence vector

Abstract

Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.

Cite This Paper

Pranoti P. Mane, Amruta B. Rathi, Narendra G. Bawane,"An Interactive Approach for Retrieval of Semantically Significant Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.3, pp.63-70, 2016.DOI: 10.5815/ijigsp.2016.03.08

Reference

[1]Y. Rui, T. S. Huang , M. Ortega and S. Mehrotra, "Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval", IEEE Trans. Circuits. Systems for Video Technology, vol. 8, no. 5, pp.644 -655 1998 

[2]Kherfi, M.L.; Ziou, D., "Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples," IEEE Transactions on Image Processing, vol.15, no.4, pp.1017,1030, April 2006

[3]Grigorova, A.; De Natale, F.G.B.; Dagli, C.; Huang, T.S., "Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback," IEEE Transactions on Multimedia, vol.9, no.6, pp.1183,1192, Oct. 2007

[4]Pranoti P. Mane, Narendra G. Bawane," Image Retrieval by Utilizing Structural Connections within an Image", I.J. Image, Graphics and Signal Processing, 2015, in Press.

[5]Saju, A.; Thusnavis, B.M.I.; Vasuki, A.; Lakshmi, P.S., " Reduction of semantic gap using relevance feedback technique in image retrieval system", Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), pp.148,153, 17-19 Feb. 2014

[6]Hong, Pengyu, Qi Tian, and Thomas S. Huang, "Incorporate support vector machines to content-based image retrieval with relevance feedback", International Conference on Image Processing, Vol. 3. IEEE, 2000.

[7]Zhou, Xiang Sean, and Thomas S. Huang, "Image retrieval: feature primitives, feature representation, and relevance feedback", IEEE Workshop on Content-based Access of Image and Video Libraries, IEEE, 2000.

[8]Han, JunWei, and Lei Guo, "A new image retrieval system supporting query by semantics and example", International Conference on Image Processing, Vol. 3. IEEE, 2002.

[9]Pushpa B.Patil , Manesh B. Kokare , "Content Based Image Retrieval with Relevance Feedback using Riemannian Manifolds",Fifth International Conference on Signals and Image Processing, pp.26,29, 8-10 Jan. 2014

[10]Boury-Brisset, Anne-Claire, "Ontology-based approach for information fusion", Proceedings of the Sixth International Conference on Information Fusion, 2003.

[11]Gorti Satyanarayana Murty,J Sasi Kiran,V.Vijaya Kumar, "Facial Expression Recognition Based on Features Derived From the Distinct LBP and GLCM", IJIGSP, vol.6, no.2, pp. 68-77, 2014.

[12]S. A. Medjahed, "A Comparative Study of Feature Extraction Methods in Images Classification", International Journal of Image Graphics and Signal Processing (IJIGSP), 7: 16-23 (2015).

[13]Xiaoqian Xu, Dah-Jye Lee, K. Sameer, L. Antani, Rodney Long, James K. Archibald, "Using relevance feedback with short-term memory for content-based spine X-ray image retrieval", Neurocomputing, 72 (2009), pp. 2259–2269

[14]Plataniotis, Konstantinos N. and Anastasios N. Venetsanopoulos, "Color image processing and applications", Springer Science and Business Media, 2000.

[15]Xiaojun Qi; Ran Chang, "A fuzzy statistical correlation-based approach to content based image retrieval", IEEE International Conference on Multimedia and Expo, pp.1265, 1268, 2008.

[16]Peng-Yeng Yin; Bhanu, B.; Kuang-Cheng Chang; Anlei Dong, "Integrating relevance feedback techniques for image retrieval using reinforcement learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, Issue 10, pp.1536,1551,Oct. 2005.

[17]Yang Mingqiang, Kpalma Kidiyo and Ronsin Joseph, "A Survey of Shape Feature Extraction Techniques, Pattern Recognition Techniques", Technology and Applications, Book edited by: Peng-Yeng Yin, pp. 626, November 2008.

[18]M.J. Swain, D.H. Ballard,"Color indexing", International Journal of Computer Vision, pp. 1132, vol. 7, Issue 1, 1991

[19]Dacheng Tao; Xiaoou Tang; Xuelong Li; Xindong Wu, "Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, Issue 7, pp. 1088,1099, July 2006.

[20]Sangoh Jeong, Chee SunWon, RobertMGray, "Image retrieval using color histograms generated by Gauss mixture vector quantization", Computer Vision and Image Understanding, Volume 94, Issues 1-3, pp. 44-66, 2004.

[21]Jing Li; Allinson, N.; Dacheng Tao; Xuelong Li, "Multitraining Support Vector Machine for Image Retrieval", IEEE Transactions on Image Processing, vol. 15, Issue 11, pp. 3597, 3601, Nov. 2006.

[22]Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, "A survey of content-based image retrieval with high-level semantics", Pattern Recognition, Volume 40, Issue 1, Pages 262-282, January 2007.

[23]Zabih Justin Miller, Greg Pass, "Comparing images using color coherence vectors", The fourth ACM International Multimedia Conference, Boston, MA, USA, pp. 65-73, 1996. 

[24]Takala, Valtteri, Timo Ahonen, and Matti Pietikinen, "Block-based methods for image retrieval using local binary patterns", Image analysis, Springer Berlin Heidelberg, pages 882-891, 2005.

[25]Menp, Topi," The Local binary pattern approach to texture analysis: Extenxions and applications", Oulun yliopisto, 2003.

[26]Karam, Omar, Ahmad Hamad, and Mahmoud Attia, "Exploring the Semantic Gap in Content-Based Image Retrieval: with application to Lung CT", ICGST-Graphics Vision and Image Processing Conference (GVIP 05 Conference), 2005.

[27]Traina, Agma J M, Joselene Marques, and Caetano Traina Jr., "Fighting the semantic gap on cbir systems through new relevance feedback techniques", 19th IEEE International Symposium on Computer-Based Medical Systems, IEEE, 2006.

[28]P. P. Mane, N. G. Bawane and A. Rathi, "Image retrieval using primitive feature extraction with hybrid approach", National Conference ACCET-15, India, pp. 99–102, 2015.

[29]J. Li and James Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach", IEEE Transactions on Pattern Analysis and Machine Intelligence, Pages 1075-1088, 2003.