Open Access Open Access  Restricted Access Subscription or Fee Access

Integrated CBIR Using Texture, Fourier Descriptor and Color Histogram

M.H. Saad, H.I. Saleh, H. Konbor, M. Ashour


Content-Based Image Retrieval (CBIR) considers the
characteristics of the image itself, for example its shapes, colors and textures. The Current approaches to CBIR differ in terms of which image features are extracted. Recent work deals with combination of
distances or scores from different and independent representations. CBIR has many application fields such as, education, commerce, military, searching, biomedicine, and web image classification. This
paper proposes a new image retrieval system, which uses color and Shape descriptions information and Texture to form the feature vectors. This framework integrates the ycbcr color histogram which represents the color feature, Fourier descriptor as shape descriptor
and Edge Histogram as texture descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations
and noise. It is tested on a standard image databases such as Wang and UCID database. Experimental work show that the proposed approach improves the precision and recall of retrieval results
compared to other approaches reported in literature.


CBIR, Image Retrieval, EHD, FCTH, MPEG7, YCbCr

Full Text:



Mohamed Ahmed Ebrahim Helala, Mazen Mohamed Selim, Hala Helmy

Zayed, "An Image Retrieval Approach Based on Composite Features

and Graph Matching," iccee, vol. 1, pp.466-473, 2009 Second

International Conference on Computer and Electrical Engineering, 2009.

Shamik Sural, Gang Qian, and Sakti Pramanik. Segmentation and

histogram generation using the hsv color space for image retrieval.

Proceedings of the 6th ACM international conference on Image and

video retrieval, Amsterdam, The Netherlands, 2:II–589– II–592, 2002.

Barcellos, R; Oliani , R. S.; Lorenzi, L. T.; Gonzaga, A. "Content Based

Image Retrieval Using Color Autocorrelograms in HSV Color Space", In

Proceedings Of Xviii Brazilian Symposium on Computer Graphics and

Image Processing - Sibgrapi 2005, ISBN: 85-7669-036-5.

Ji-quan Ma, "Content-Based Image Retrieval with HSV Color Space and

Texture Features," wism, pp.61-63, 2009 International Conference on

Web Information Systems and Mining, 2009

M. H. Saad, H. I. Saleh, H. Konbor, M. Ashour, " Image Retrieval based

on Integration between YCbCr Color Histogram and Texture Feature

", to be published at international journal of computer theory and

engineering ( IJCTE) in Vol. 3, No. 5, 2011.

Ka-Man Wong, Kwok-Wai Cheung and Lai-Man Po, “MIRROR: an

interactive content based image retrieval system”, Proceedings of IEEE

International Symposium on Circuit and Systems, vol. 2, Japan, May

, pp. 1541-1544, 23-26.

Jose M. Martinez, Mpeg-7 overview


Qasim Iqbal and J.K. Aggarwal. Combining structure, color and texture

for image retrieval: A performance evaluation. 16th International

Conference on Pattern Recognition (ICPR), 2:438–443, 2002.

S. A. Chatzichristo_s and Y. S. Boutalis, “FCTH - Fuzzy Color and

Texture Histogram - A low level Feature for Accurate Image Retrieval",

th International Workshop on Image Analysis for Multimedia

Interactive Services, IEEE Computer Society, Klagenfurt, Austria, 2008,

pp. 191-196.

D. Zhang and G. Lu.," An Integrated Approach to Shape Based

Image Retrieval", ACCV2002: The 5th Asian Conference on Computer

Vision, 23—25 January 2002.

D. Zhang and G. Lu.," Content-Based Shape Retrieva Using Different

Shape Descriptors: A Comparative Study". In Proc. of IEEE Conference

on Multimedia and Expo (ICME’01), Tokyo, August, 2001.

Keinosuke Fukunaga. Introduction to statistical pattern recognition (2nd

ed.)Academic Press. 1990.

Sylvie Philipp-Foliguet, Julien Gony, and Philippe-Henri Gosselin.

Frebir: An image retrieval system based on fuzzy region matching.

Comput. Vis. Image Underst., 113(6):693–707, 2009.

Zhang H. LongF. and Dagan Feng D. Fundamentals of content-based

image retrieval,in multimedia information retrieval and management –

technological fundamentals and applications. Springer-Verlag, pages 1–

, 2003.

J. Stottinger, N. Sebe, T. Gevers, and A. Hanbury. Colour interest points

for image retrieval. Vision Winter Workshop, 2007.

James Z. Wang, Jia Li and Gio Wiederhold, “SIMPLIcity: Semantics-

Sensitive Integrate, Matching for Picture Libraries”, IEEE transactions

on pattern analysis and machine intelligence, Volume 23, no. 9,

September 2001.

G. Schaefer and M. Stich, “UCID -An Uncompressed Colour Image

Database”, Proc. SPIE, Storage and Retrieval Methods and Applications

for Multimedia, pp. 472-480, San Jose, USA 2004.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.