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

Spatial Layout Image Retrieval based on Fast Image Segmentation using K-Means Clustering

by D. Binu, P. Malathi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 4
Year of Publication: 2012
Authors: D. Binu, P. Malathi
10.5120/7613-0657

D. Binu, P. Malathi . Spatial Layout Image Retrieval based on Fast Image Segmentation using K-Means Clustering. International Journal of Computer Applications. 49, 4 ( July 2012), 6-10. DOI=10.5120/7613-0657

@article{ 10.5120/7613-0657,
author = { D. Binu, P. Malathi },
title = { Spatial Layout Image Retrieval based on Fast Image Segmentation using K-Means Clustering },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 4 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number4/7613-0657/ },
doi = { 10.5120/7613-0657 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:45:47.155187+05:30
%A D. Binu
%A P. Malathi
%T Spatial Layout Image Retrieval based on Fast Image Segmentation using K-Means Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 4
%P 6-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An image retrieval system that takes the input query image and retrieves the similar images according to the spatial coordinates and which uses the k means clustering algorithm for its segmentation. Most existing Content Based Image Retrieval based on the images of color, text documents, informative charts, and shape. This paper aims to search the images with similar spatial layouts and the retrieval process which includes the feature extraction methods and image matching criteria. This paper employs K-Means clustering for image segmentation, and the features of small segments of images are extracted based on the segmentation results. To match the segments of two images, the distance between them can be computed by Eucliedean distance to evaluate the similarity of their spatial layouts.

References
  1. Y. Rui, T. S. Huang, and S. -F. Chang, "Image retrieval: Current techniques, promising directions, and open issues," Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39–62, Mar. 1999.
  2. Tse-Wei Chen,Yi-Ling Chen,and Shao-Yi Chien,"Photo Retrieval Based on Spatial Layout with Hardware Acceleration for Mobile Devices",IEEE Transactoins on Mobile Computing,2011,pp. 1-15.
  3. M. Luo, Y. -F. Ma and H. -J. Zhang, "A spatial constrained K-Means approach to image segmentation," in Proceedings of the Joint Conference of International Conference on Information, Communications and Signal Processing, and Pacific Rim Conference on Multimedia, vol. 2, Dec. 2003, pp. 738–742.
  4. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, "An efficient K-Means clustering algorithm: analysis and implementation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881–892, July 2002.
  5. B. K¨ovesi, J. -M. Boucher, and S. Saoudib, "Stochastic K-Means algorithm for vector quantization," Pattern Recognition Letters, vol. 22, no. 6, pp. 603–610, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

image retrieval image segmentation k-means clustering spatial layout

Powered by PhDFocusTM