Image Retrieval by Utilizing Structural Connections within an Image

Full Text (PDF, 538KB), PP.68-74

Views: 0 Downloads: 0


Pranoti P. Mane 1,* Narendra G. Bawane 2

1. Department of Electronics and Telecommunication, M.E.S. College of Engineering, SPPU, Pune, 411001, India

2. S.B. Jain Institute of Technology, Management & Research, Nagpur, 441501, India

* Corresponding author.


Received: 9 Sep. 2015 / Revised: 14 Oct. 2015 / Accepted: 19 Nov. 2015 / Published: 8 Jan. 2016

Index Terms

Content-based image retrieval, color coherence vector, local binary pattern, semantic gap, precision


Content-based image retrieval (CBIR) is broadly applicable for searching digital images from a gigantic database. Images are retrieved by their primitive visual contents such as color, texture, shape, and spatial layout. The approach presented in this paper utilizes structural connections within an image by integrating textured color descriptors and structure descriptors to retrieve semantically significant images. The retrieval results were obtained by applying the HSV histogram, color coherence vector, and local binary pattern histogram to the standard database of Wang et al., which has 1000 images of 10 different semantic categories. Euclidean distance was used to find the similarity between the query image and database images. This method was evaluated against different methods based on edge histogram descriptors, color structure descriptors, color moments, the color histogram, the HSV histogram, Tamura features, edge descriptors, geometrical shape attributes, and statistical properties such as mean, variance, skewness, and kurtosis. Retrieval results obtained using the proposed methods demonstrated a significant improvement in the average precision (73.8% and 73.1%) compared with those obtained using other existing retrieval methods.

Cite This Paper

Pranoti P. Mane, Narendra G. Bawane,"Image Retrieval by Utilizing Structural Connections within an Image", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.1, pp.68-74, 2016. DOI: 10.5815/ijigsp.2016.01.08


[1]M. B. Kokare, B. N. Chatterji and P. K. Biswas, "A Survey on Current Content Based Image Retrieval Methods", IETE Journal of Research, pp. 261-271, 2002.

[2]Y. Liu, D. Zhang, G. Lu and W. Y. Ma, "A survey of content-based image retrieval with high-level semantics", Pattern Recogn. vol.40, issue1, pp. 262-282, January 2007.

[3]Pranoti Mane and Dr. N. G. Bawane, "Comparative Performance Evaluation of Edge Histogram Descriptors and Color Structure Descriptors in Content Based Image Retrieval", IJCA Proceedings on NCIPET 2013, No.6, pp. 5-9, December 2013.

[4]G. Rafiee, S. S. Dlay, and W. L. Woo, "A Review of Content-Based Image Retrieval", CSSNDSP 2010, pp.775-779, 2010.

[5]Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta,and Ramesh Jain, "Content-based image retrieval at the end of the early years.", IEEE Trans. Pattern Anal. Mach. Intell., 22(12), pp.1349–1380, 2000.

[6]T. Tsai, Y. P. Huang and T. W. Chiang, "A fast two-stage content-based image retrieval approach in the DCT domain", Intern. J. Pattern Recognit. Artif. Intell, vol.28, No. 4, pp. 765–781, 2008.

[7]Mohamed M. Fouad, "Content-based Search for Image Retrieval", IJIGSP, vol.5, no.11, pp.46-52, 2013.

[8]W.I. Grosky and R. Agrawal, "Narrowing the Semantic Gap in Image Retrieval: A Multimodal Approach", Multimedia Information Extraction and Digital Heritage Preservation, U. M. Munshi and B. B. Chaudhuri (Eds.), World Scientific, pp. 89-118. 

[9]O. Karam, A. Hamad and M. Attia, "Exploring the semantic gap in CBIR: with application to lung CT", GVIP Conference (Cairo, Egypt), pp. 422–426, 2005.

[10]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.

[11]A. J. M. Traina, J. Marques and C. Traina Jr, "Fighting the semantic gap on cbir systems through new relevance feedback techniques", Computer-Based Medical Systems, 19th IEEE International Symposium on, pp. 881–886, 2006.

[12]S.Maruthuperumal,G. Rosline Nesa Kumari, "A New Method for Content based Image Retrieval using Primitive Features", IJMECS, vol.5, no.10, pp.36-42, 2013.

[13]K. Prasanthi Jasmine, P. Rajesh Kumar,"Color Histogram and DBC Co-Occurrence Matrix for Content Based Image Retrieval", IJIEEB, vol.6, no.6, pp.47-54, 2014.

[14]M. Flickner, et al., "Query by image and video content: The QBIC system", Computer, vol. 28, No.9, pp. 23–32, 1995.

[15]R. Zabih, J. Miller and G. Pass, "Comparing images using color coherence vectors", Proceedings of the fourth ACM international conference on Multimedia, pp. 65–73, 1996.

[16]D. Zhang, Y. Liu and J. Hou, "Digital image retrieval using intermediate semantic features and multistep search", DICTA, 513–518, 2008.

[17]J. Li, N. Allinson, D. Tao and X. Li, "Multitraining support vector machine for image retrieval", IEEE Trans. Image Process, vol.15, No.11, pp. 3597–3601, 2006.

[18]P. Hong, Q. Tian, and T. S. Huang, "Incorporate support vector machines to content-based image retrieval with relevant feedback", Image Processing, Proceedings, International Conference on., vol. 3, pp. 750–753, 2000.

[19]P. H. Gosselin and M. Cord, "Semantic kernel updating for content-based image retrieval", Multimedia Software Engineering, 2004. Proceedings, IEEE Sixth International Symposium on, pp. 537–544, 2004.

[20]Y. Rui, T. S. Huang, M. Ortega and S. Mehrotra, "Relevance feedback: A power tool for interactive content-based image retrieval", IEEE Trans. Circuits Syst. Video Technol, vol. 8, No.5, pp. 644–655, 1998.

[21]M. Broilo and F. G. B. De Natale, "A stochastic approach to image retrieval using relevance feedback and particle swarm optimization", Multimedia, IEEE transactions on, vol. 12, No. 4, 267–277, 2010.

[22]W. Jiang, G. Er, Q. Dai and J. Gu, "Similarity-based online feature selection in content-based image retrieval, IEEE Trans. Image Process, vol. 15, No.3, pp. 702–712, 2006.

[23]W. Liu, W. Jiang and S. F. Chang, "Relevance aggregation projections for image retrieval", Proceedings of the 2008 international conference on Content-based image and video retrieval, pp. 119–126, 2008.

[24]W. Bian and D. Tao, "Biased discriminant Euclidean embedding for content-based image retrieval", Image Process, IEEE Transactions on, vol. 19, No. 2, pp. 545–554, 2010.

[25]S. Joseph and K. Balakrishnan, "Multi-query content based image retrieval system using local binary patterns", International Journal of Computer Applications, vol. 17, No. 7, pp. 1-5, 2011.

[26]T. Mäenpää and M. Pietikäinen, "Texture analysis with local binary patterns", Handbook of Pattern Recognition and Computer Vision, vol. 3, pp. 197–216, 2005.

[27]V. Takala, T. Ahoven and M. Pietikainen, "Block-based methods for image retrieval using local binary patterns", Image Analysis, pp. 882–891, 2005.

[28]B. S. Manjunath, J. R. Ohm, V. V. Vasudevan and A. Yamada, "Color and texture descriptors", IEEE Trans. Circuits Syst. Video Techno, vol. 11, No. 6, pp. 703–715, 2001.

[29]Y J. R. Ohm, et al., "Color Descriptors, Introduction to MPEG-7: Multimedia Content Description Interface", eds. P. Salembier et al. (John Wiley & Sons, New York, 2002), pp. 187–212.

[30]D. K. Park, Y. S. Jeon and C. S. Won, "Efficient use of local edge histogram descriptor", Proceedings of the 2000 ACM workshops on Multimedia, pp. 51–54, 2000.

[31]T. Sikora, "The MPEG-7 visual standards for content description-an overview", IEEE. Trans. Circuits. Syst. Video. Technol., vol. 11, No. 6, pp. 696–702, 2001.

[32]C. S. Won, D. K. Park and S. J. Park, "Efficient use of MPEG-7 edge histogram descriptor", ETRI Journal, vol. 24, No. 1, pp. 23–30, 2002.

[33]N. Singh, S. Dubey, P. Dixit and J. P. Gupta, "Semantic image retrieval by combining color, texture, and shape features", Computing Sciences (ICCS), IEEE International Conference on. 2012, pp. 116–120, 2012.

[34]P. P. Mane and N. G. Bawane, "An approach to explore the role of color models and color descriptors in the optimization of semantic gap in content based image retrieval", International Journal of Computer Applications, vol. 104, No. 14, pp. 9–16, 2014.

[35]P. P. Mane and N. G. Bawane, Optimization of gap between visual features and high level human semantics in content based image retrieval, SCITECH, Pune, India, 2012.

[36]J. Z. Wang, James Z. Wang Research Group, Available at