CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Improvement in Performance of Image Retrieval using Various Features in CBIR System

by Dipesh Patel, Darshan Patel
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 11
Year of Publication: 2016
Authors: Dipesh Patel, Darshan Patel
10.5120/ijca2016909005

Dipesh Patel, Darshan Patel . Improvement in Performance of Image Retrieval using Various Features in CBIR System. International Journal of Computer Applications. 138, 11 ( March 2016), 17-20. DOI=10.5120/ijca2016909005

@article{ 10.5120/ijca2016909005,
author = { Dipesh Patel, Darshan Patel },
title = { Improvement in Performance of Image Retrieval using Various Features in CBIR System },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 11 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number11/24423-2016909005/ },
doi = { 10.5120/ijca2016909005 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:24.860789+05:30
%A Dipesh Patel
%A Darshan Patel
%T Improvement in Performance of Image Retrieval using Various Features in CBIR System
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 11
%P 17-20
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content-Based image retrieval systems (CBIR) have become very popular for browsing, searching, and retrieving images from a large database of digital images as it requires relatively less human interference. In Content-based image retrieval system, visual feature. Color, texture and shape features have been the primitive image descriptors in CBIR systems. By using only color, texture or shape features, cannot get high precision. So, propose a new content-based image retrieval method that uses combination of color, shape and texture feature to get high precision. By using techniques like Image Processing, Data Mining, Machine Learning and Database for extracting color features, texture features and shape features, In this paper discuss the using various features and technique to possible get best precision as well as less computational complexity and good retrieval accuracy.

References
  1. Jiawei Han University of Illinois at Urbana-Champaign MichelineKamber, “Data Mining Concepts and Techniques.2nd.
  2. A.Hema1, E.Annasaro2Nuaimi, “A Survey in need of image mining techniques” (February 2013).International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 2, February 2013 page 1238-1240
  3. JiZhang,WynneHsu,Mong Li Lee “Image minig: Issue,Framework and techniques”
  4. RamadassSudhir, “A Survey on Image Mining Techniques: Theory and Applications” Computer Engineering and Intelligent Systems ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 2, No.6, 2011
  5. A.Kannan, Dr.V.Mohan, Dr.N.Anbazhagan “An Effective Method of Image Retrieval using Image Mining Techniques” (November 2010).The International journal of Multimedia & Its Applications (IJMA) Vol.2, No.4.
  6. Monika Sahu, MadhupShrivastava “Image mining: A new approach for data mining based on texture”2012 Third International Conference on Computer and Communication Technology IEEE page 7-9
  7. S.Pradeep, Mrs.L.Malliga “Content based image retrieval and segmentation of medical image database with fuzzy” 2014 IEEE ICICES 2014
  8. Ahsan Raza Sheikh, SarinaMansor, Mohd. H. Lye ,Mohd. F. A. Fauzi“Content Based Image Retrieval System for Marine Life Images using Gradient Vextore Flow ” 2013 IEEE International Conference on Signal and Image Processing Applications (lCSIPA)
  9. Ricardo da Silva Torres, Alexandre Xavier Falcão“Content-Based Image Retrieval: Theory and Applications” 2006 Institute of Computing, State University of Campinas, Campinas, SP, Brazil. Volume XIII, page 165-189
  10. Nadia Baaziz, Omar Abahmane and RokiaMissaoui “Texture feature extraction in the spatial-frequency domain for content-based image retrieval”.
  11. Vivek Jain, NehaSahu “A Survey: On Content Based Image Retrieval”Vol. 3, Issue 4, Jul-Aug 2013, pp.1166-1169
  12. S.Asha, S.Bhuvana, Dr.R.Radhakrishnan“A Survey on Content Based Image Retrieval Based on Feature Extraction” 2014 Vol 1, Issue 06;pp 29-34
  13. ManasSaha, Snigdhadeb Roy Chowdhury, Kyamelia Roy “Ranklets: A Qualitative Review” September 2011pp 68 73.
  14. Ahmed J. Afifi, Wesam M. Ashour, “Content-Based Image Retrieval Using Invariant Color and Texture Features”, IEEE 2012
  15. M.E. ElAlami, “A new matching strategy for content based image retrieval system”, Elsevier October 2013
  16. A.A. Khodaskar, S. A. Ladhake, “A Novel Approach for Content Based Image Retrieval in context of combination S C Techniques”, IEEE October 2015
  17. RoshiChoudhary, Nikita Raina, Neeshu Chaudhary, Rashmi Chauhan, Dr. R H Goudar, “An Integrated Approach to Content Based Image Retrieval”, IEEE 2014.
  18. Sudeep D. Thepade , Yogita D. Shinde “Improvisation of Content Based Image retrieval using Color Edge Detection with various Gradient Filters and Slope Magnitude Method” 2015 International Conference on Computing Communication Control and Automation 2015-IE
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Image Mining Content Based Image Retrieval Feature extraction Image retrieval.

Powered by PhDFocusTM