The Multiple Feature Extraction in CBIR with Relevance Feedback

  • Prajakta R .Thakare
Keywords: CBIR; color; texture; feature extraction; relevance feedback

Abstract

Rapid expansion of image data on internet opens the way to image retrieval systems. Most commercial image retrieval systems include text based approach which is incompetent as some features are nearly impossible to describe with text. In an effort to overcome these problems and to improve image retrieval performance researchers are focusing on Content Based Image Retrieval (CBIR). CBIR system uses the contents of image such as color, shape and texture features. This paper presents superior feature extraction technique in CBIR using multiple features like color and texture than the single feature. The performance of CBIR systems is further enhanced by relevance feedback (RF) mechanism that incorporates human perception subjectivity into the query process and provides users with opportunity to evaluate retrieval results.

References

[1] D. Brahmi and D. Ziou, “Improving CBIR systems by integrating semantic features” Proceedings of the First IEEECanadian Conference on Computer and Robot Vision (CRV’04)
[2] P. S. Hiremath , Jagadeesh Pujari, “Content Based Image Retrieval using Color, Texture and Shape features.” 15th IEEE International Conference on Advanced Computing and Communications
[3] K. Satya Sai Prakash1, RMD. Sundaram, “Combining Novel features for Content Based Image Retrieval
[4] Khadidja BELATTAR, Sihem MOSTEFAI “CBIR using Relevance Feedback:Comparative Analysis and Major Challenges,” 5th IEEE International Conference on Computer Science and Information Technology (CSIT).
[5] Sowmya Rani , Rajani N., and Swathi Reddy, “Comparative Study on Content Based Image Retrieval” International Journal of Future Computer and Communication, Vol. 1, No. 4, December 2012
[6] Mohamed A. Tahoun', Khaled A. Nagag, Taha I. El-Arie, Mohammed A-Megeed3,”A Robust Content-Based Image Retrieval System Using Multiple Features Representations” Proceedings of IEEE Transactions 2005
[7] Seong-O Shim, Tae-Sun Choi ”Edge color Histogram for Image Retrieval ”, Proccedings of IEEE Transactions 2002
[8] K. Satya Sai Prakash1, RMD. Sundaram, “ Combining Novel features for Content Based Image Retrieval”
[9] Dengsheng Zhang, “Improving Image Retrieval Performance by Using Both Color and Texture Features” In Proceedings of the IEEE Third
[10] Sanjoy Kumar Saha, Amit Kumar Das, Bhabatosh Chanda, “CBIR using Perception based Texture and Colour Measures”, Proceedings of international conference on Pattern Recognition,2004.
Published
2018-04-15
How to Cite
.Thakare, P. (2018, April 15). The Multiple Feature Extraction in CBIR with Relevance Feedback. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(I). https://doi.org/https://doi.org/10.33130/asian%20journals.v4iI.415
Section
Electronics and Telecommunication