Fast Content Based Color Image Retrieval System Based on Texture Analysis of Edge Map

Article Preview

Abstract:

In this paper we propose a method for CBIR based on the combination of texture, edge map and color. As texture of edges yields important information about the images, we utilized an adaptive edge detector that produces a binary edge image. Also, using the statistics of color in two different color spaces provides complementary information to retrieve images. Our method is time efficient since we have applied texture calculations on the binary edge image. Our experimental results showed both the higher accuracy and lower time complexity of our method with similar related works using SIMPLIcity database.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 341-342)

Pages:

168-172

Citation:

Online since:

September 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] R.S. Torres, A.X. Falcão, Content-Based image retrieval: Theory and Applications, RITA, Volume XIII, Número 2, (2006).

Google Scholar

[2] S. Feng, D. Xu, X. Yang, Attention-driven salient edge(s) and region(s) extraction with application to CBIR, Signal Processing 90, p.1–15, (2010).

DOI: 10.1016/j.sigpro.2009.05.017

Google Scholar

[3] H. Abrishami Moghaddam, T Taghizadeh Khajoie, A.H. Rouhi, M. Saadatmand Tarzjan, Wavelet Correlogram: A new approach for image indexing and retrieval, Pattern Recognition 38, p.2506–2518, (2005).

DOI: 10.1016/j.patcog.2005.05.010

Google Scholar

[4] L.G. Shaprio, G.C. Stockman, Computer Vision, Prentice Hall, (2001).

Google Scholar

[5] D.A. Cluasi, H. Deng, Fusion of Gabor Filter and Co-occurrence Probability Features for Texture Recognition, IEEE Transactions on Image Processing, Vol. 14, No. 7, pp.925-936, (2005).

DOI: 10.1109/tip.2005.849319

Google Scholar

[6] A. Vailaya, A. Jain, H. J Zhang, On Image Classification: City Images vs. Landscape, Proceeding of the IEEE workshop on Content-Based Access of Image and Video Libraries, pp.3-8, (1998).

DOI: 10.1109/ivl.1998.694464

Google Scholar

[7] J. Shanbehzadeh, F. Mahmoudi, A. Sarafzadeh, A.M. Eftekhari-Moghaddam, Image Retrieval Based on the Directional Edge Similarity, Proceeding of the SPIE: Multimedia Storage and Archiving Systems, Vol. IV, Boston, Massachusetts, USA, pp.267-271, (1999).

DOI: 10.1117/12.360430

Google Scholar

[8] F. Mahmoudi, J. Shanbehzadeh, A.M. Eftekhari-Moghaddam, H. Soltanian-Zadeh, Image Retrieval Based on Shape similarity by edge orientation autocorrelogram, Pattern Recognition 36, p.1725–1736. (2003).

DOI: 10.1016/s0031-3203(03)00010-4

Google Scholar

[9] R. Kaur, M. Verma, Kalpna, H. Kundra, Classification of Various Edge Detectors.

Google Scholar

[10] A.K. Jain, F. Farrokhnia, Unsupervised Texture Segmentation Using Gabor Filters, Pattern Recognition, Vol. 24, No. 12. pp.167-1186, (1991).

DOI: 10.1016/0031-3203(91)90143-s

Google Scholar

[11] T. Ojala, M. Rautiainen, E. Matinmikko, M. Aittola, Semantic Image Retrieval with HSV Correlograms , Proceeding of the 12th Scandinavian conference on image analysis, Bergen, Norway, pp.621-627, (2001).

Google Scholar