CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Content based Image Processing using Relevance Feedback with Null Space LDA (NLDA)

by C. Rajivegandhi, V. Murugesh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 5
Year of Publication: 2012
Authors: C. Rajivegandhi, V. Murugesh
10.5120/9692-4133

C. Rajivegandhi, V. Murugesh . Content based Image Processing using Relevance Feedback with Null Space LDA (NLDA). International Journal of Computer Applications. 60, 5 ( December 2012), 42-45. DOI=10.5120/9692-4133

@article{ 10.5120/9692-4133,
author = { C. Rajivegandhi, V. Murugesh },
title = { Content based Image Processing using Relevance Feedback with Null Space LDA (NLDA) },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 5 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number5/9692-4133/ },
doi = { 10.5120/9692-4133 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:52.949951+05:30
%A C. Rajivegandhi
%A V. Murugesh
%T Content based Image Processing using Relevance Feedback with Null Space LDA (NLDA)
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 5
%P 42-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The biggest problem in the research of Content Based Image Retrieval (CBIR) is bridge the gap between low-level features and high-level semantics. , Still many shortcomings for image retrieval system only with the low level visual features due to the semantic space. It is better for the relevance feedback based on the user involvement in image retrieval system. By using the help of user's feedback, the resultant high-level semantic will be obtained. Relevance feedback is a technique for incorporating semantic information in image retrieval. This paper illustrates a development upon a relevance feedback approach that utilizes semantic grouping and clustering technique to close the distance between low-level features and high-level semantics. Distinctively, the past system is improved by incorporating the images in the same group as the query image in the collection of retrieved images. Shared the retrieval results with relevance feedback technology, image feature dimensional reduction was prepared using the Clustering concepts. The given system reduces semantic gap and the storage of image signatures, and also improves the retrieval efficiency and performance. The result shows the efficiency of our proposed system.

References
  1. R. Datta, D. Joshi, J. Li, and J. Z. WANG, "Image retrieval: Ideas, influences, and trends of the new age," ACM Computing Surveys, vol. 40, no. 2, 2008, pp. 1–60 .
  2. Smith, J. R. and Chang S-F. Transform features for texture classi¯cation and discrimination in large image databases. In Proceedings of IEEE Intl. Conf. on Image Processing, 1994
  3. Stricker, M. and Orengo, M. Similarity of color images. In Proceedings of SPIE, 1995
  4. T. Yoshizawa and H. Schweitzer. Long-term learning of semantic grouping from relevance- Information Retrival, pages 165 {172, New York, NY, USA, 2004. ACM.
  5. Zhang Jing, Shen Lan-sun, David Dagan Feng. "A survey of image retrieval based on visual perception", J. ACTA ELECTRONICA SINICA, vol. 36, no. 3, pp. 494-499, 2008.
  6. Zhou, X. S. and Huang, T. S. Edge-based structural feature for content-base image retrieval. Pattern Recognition Letters, Special issue on Image and Video Indexing, 2000.
  7. Yu Bing, Jin Lianfu, Chen Ping. A new lda-based method for face recognition. Pattern Recognition, 2002.
  8. Y. Chen and J. Z. Wang, "A region-based fuzzy feature matching approach to content-based image retrieval," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 24, no. 9, 2002, pp. 1252–1267.
  9. Y. Chen and J. Z. Wang, "Image categorization by learning and reasoning with regions," Journal of Machine Learning Research, vol. 5,2004, pp. 913-939.
Index Terms

Computer Science
Information Sciences

Keywords

Content Based Image Processing Semantic Grouping Null Space LDA

Powered by PhDFocusTM