A Frame Work for Reducing the Time for Image Retrieval with Genetic Algorithm

Authors

  • S. SELVAM Research Scholar and Assistant Professor, Dept. of Computer Applications, N.M.S.S.V.N. College, Madurai-19
  • S. THABASUKANNAN Principal, Pannai College of Engg& Tech, Sivagangai – 630 561, Tamilnadu, India.

DOI:

https://doi.org/10.51983/ajcst-2014.3.2.1735

Keywords:

CBIR, Genetic Algorithm, HARP Algorithm, Precision, Recall

Abstract

In recent years especially in the last decade, the rapid development in computers, storage media and digital image capturing devices enable to collect a large number of digital information and store them in computer readable formats. The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and effi ciently. Although this area has been explored for decade sand many researches have been done to develop some algorithms that solve some of its problems, no technique has achieved the accuracy of human visual perception indistinguishing images. Nowadays, virtually all domains of human life including commerce, government, academics, hospitals, crime prevention, surveillance, engineering and historical research use information as images, so the volume of digital data is increasing rapidly. These images and their data are categorized and stored on computers and the problem appears when retrieving these images from storage media. Thus CBIR from large resources has become an area of wide interest in recent years especially in the last decade. To retrieve any image, we have to search for it among the database using some search engine. Then, this search engine will retrieve many of images related to the searched one. The main problem fo r t he user is the difficulty of locating his relevant image in this large and varied collection of resulted images. To solve this problem, text-based and content-based are the two techniques adopted for search and retrieval.
The main objective of this paper is to build more generalized CBIR system which increase the searching ability and provide more accurate results. To improve the retrieval accuracy the system has taken the feedback from the user automatically. Here we used WANG database to evaluate the performance of the new system by calculating the precision and recall metrics. We also compared the new system with other existing CBIR systems. The performance of the new architecture in terms of average precision, recall and retrieval time has been shown to perform good. From he experimental results, it is evident that the new system has beaten other existing systems in terms retrieval time.

References

V.GudivadaandV.Raghavan,“Content-basedImageRetrievalSystem”IE EEComputer, vol. 28, no 9, pp18-22, Sep. 1995.

F.Long,H.Zhang,H.Dagan,andD.Feng, “Fundamentals of Content Based Image Retrieval,” Multimedia Signal Processing Book, Chapter1,Springer-Verlag,BerlinHeidelberg NewYork,2003.

R . C h a n g , J . H o , S . L i n , C . F a n n a n d Y. Wa n g , “ A N o velContentBasedImageRetrievalSystemusing K-means with Feature Extraction, ”International Conference on Systems and Informatics,2012.

I.El-Naqa, Y.Yang,N.Galatsanos, R.Nishikawa and M.Wernick ,“A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography,” IEEE Transactions on Medical Imaging, 2009.

B.WANG,X.ZHANG,andN.LI,“Relevance Feedback Technique For Content-Based Image Retrieval Using Neural Network Learning ”Proceedings of the Fifth International Conference on Machine Learning and Cybernetics,Dalian, 2006.

R.Datta,J.Li,andJ.Wang,“Content-BasedImageRetrieval- ApproachesandTrends of the New Age,”ACMComputing Surveys, vol. 40, no. 2, pp. 1-60,April 2008.

J.HanandM.Kambr,“DataMiningConceptsandTe chniques,”2ndEd.,M organKaufmannPublisher, 2006.

S.Selvam and Dr.S.Thabasu Kannan, “Design of an Effective Method for Image Retrieval”, published IJIRAE, International Journal of Innovative Research in Advanced Engineering, Volume-1, March 2014, pp.51-56.

P.JeyanthiandV.JawaharSenthilKumar, “Image Classifi cationbyKmeansClustering.” Advances in Computational Sciences and Technology, 2010.

Petteri Kerminen, Moncef Gabbouj, The Visual Goodness Evaluation of Colors Based Retrieval Processes.

M. J. Swain and D. H. Ballard, Indexing via Color Histograms, ICCV’90, pp. 390- 393, 1990.

Tat-Seng Chua, Wai-Chee Low, and Chun-Xin Chu, Relevance feedback techniques for color-based image retrieval, In Proceedings of Multi-Media Modeling’98, IEEE Computer Society, pp 24-31, 2011.

M. J. Swain and D. H. Ballard, Color indexing, International Journal on Computer Vision, vol. 7, no. 1, pp. 11--32, 2011.

V. E. Ogle and M. Stonebraker, Chabot: retrieval from a relational database of images, IEEE Computer, vol. 28, no. 9, pp. 40-8, Sept. 1995.

Michael Ortega, Yong Rui, Kaushik Chakrabarti, Sharad Mehrotra and Thomas S. Huang, Supporting Similarity Queries. In Proceeding of the ACM International Multimedia Conference, pp. 403-413, 2013.

Downloads

Published

05-11-2014

How to Cite

SELVAM, S., & THABASUKANNAN, S. (2014). A Frame Work for Reducing the Time for Image Retrieval with Genetic Algorithm. Asian Journal of Computer Science and Technology, 3(2), 28–33. https://doi.org/10.51983/ajcst-2014.3.2.1735