Total views : 395

A Content-Based Indexing System for Image Retrieval


  • SGV University, Jaipur - 302017, Rajasthan, India
  • Rayat Bahra Royal Institute of Management and Technology, Sonipat, Haryana, India


Background/Objective: The aim of this research paper is to create an image indexing system by identifying and explaining image features. In this research work we are developing an image indexing algorithm. Method/Analysis: From the previous researches we select several features that can be considered suitable and can be implemented with the help of Global feature – Boolean Edge Density, Edge Density, Color Sigma, Edge Direction, Color Average and Region feature – Moment Invariant, Grey Level, Region Area. Finding: We identified best combinations for different image data set. The experiments show that region based features increase the performance of image retrieval. Application/Improvement: While comparing the two image features, i.e. global features are less substantial than the region features.


Binary Threshold, Global and Region Features, Image Retrieval, Image Indexing, K-Means Clustering.

Full Text:

 |  (PDF views: 240)


  • Kato T. Database architecture for content-based image retrieval, Image Storage and Retrieval Systems, SPIE. 1992; 1662(1):112–23.
  • Eakins J, Graham M. Content-based Image Retrieval, A report to the JISC technology applications programme, Institute for image database research, University of Northumbria at Newcastle, 1999 Oct.
  • Jiexian Z, Xiupeng L, Yu F. Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval. The Scientific World Journal. 2014; (2014):1–13.
  • Verma P, Mahajan M. Retrieval of better results by using shape techniques for content based retrieval, IJCSC. 2012 Jan-Jun; 3(2):254–7.
  • Zhang D. Improving Image Retrieval Performance by Using Both Color and Texture Features, In Proceedings of IEEE 3rd International Conference on Image and Graphics (ICIG04), Hong Kong, China, 2004 Dec 18-20. p. 172–75.
  • Yoo HW, Janga DS, Junga SH, Parka JH, Songb KS. Visual information retrieval system via content-based approach, Elsevier Pattern Recognition. 2002 Mar; 749–69.
  • Lande MV, Bhanodiya P, Jain P. An Effective Content base image retrieval using color, texture and shape feature intelligent computing, networking and informatics. Advances in Intelligent System and Computing. Springer India. 2014; 243:1163–70.
  • Wang JZ, Li J, Wiederhold G. SIMPLIcity: Semantics-sensitive integrated matching for picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001; 23(9):947–63.
  • Zhang R, Zhang Z. A clustering based approach to efficient image retrieval. Proceedings 14th IEEE Int Conference Tools with Artificial Intell (ICTAI’02), Washington, DC. 2002. p. 339–46.
  • Swain MJ, Ballard DH. Color indexing. In JComputer Vision. 1991; 7(1):11–2.
  • Zhang C, Hu L. Study on Content-Based of Image Retrieval LISS 2013. 2015; 591–94.
  • Jain AK, Vailaya A. Image Retrieval using Color and Shape. Second Asian Conference on Computer Vision. Singapore. 1995 Dec 5-8. p. 529–33.
  • Sajjanhar A, Lu G. Region-based shape representation and similarity measure suitable for content-based image retrieval. Multimedia Systems. 1999; 7(2):165–74.
  • Binu D, Malathi P. Multi Model based Biometric Image Retrieval for Enhancing Security. Indian Journal of Science and Technology. 2015 Dec; 8(35). Doi: 10.17485/ijst/2015/v8i35/81011.
  • Sasi KM, Kumaraswamy YS. A boosting frame work for improved content based image retrieval. Indian Journal of Science and Technology. 2013 Apr; 6(4):4312–6. Doi: 10.17485/ijst/2013/v6i4/31859.
  • Lakshmi DR, Damodaram A, Sreenivasa RM, Lal JAC. Content based image retrieval using signature based similarity search. Indian Journal of Science and Technology. 2008 Oct; 1(5). Doi: 10.17485/ijst/2008/v1i5/29352.
  • Jeyakumar V, Raja KB. Performance Evaluation of Image Retrieval System Based on Error Metrics. Indian Journal of Science and Technology. 2015 Apr; 8(S7):117–21. DOI:10.17485/ijst/2015/v8iS7/64950.
  • Sheshasayee A, Sharmila P. Comparative Study of Fuzzy C Means and K Means Algorithm for Requirements Clustering. Indian Journal of Science and Technology. 2014 Jan; 7(6):853–7. Doi: 10.17485/ijst/2014/v7i6/47757.


  • There are currently no refbacks.

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