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Reseach Article

Combination of Complementary Features for Automatic Image Annotation

by Rekhil M Kumar, Sreekumar K
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 19
Year of Publication: 2015
Authors: Rekhil M Kumar, Sreekumar K
10.5120/21809-5128

Rekhil M Kumar, Sreekumar K . Combination of Complementary Features for Automatic Image Annotation. International Journal of Computer Applications. 122, 19 ( July 2015), 21-27. DOI=10.5120/21809-5128

@article{ 10.5120/21809-5128,
author = { Rekhil M Kumar, Sreekumar K },
title = { Combination of Complementary Features for Automatic Image Annotation },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 19 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number19/21809-5128/ },
doi = { 10.5120/21809-5128 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:58.637646+05:30
%A Rekhil M Kumar
%A Sreekumar K
%T Combination of Complementary Features for Automatic Image Annotation
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 19
%P 21-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image annotation is a method for representing an image with a suitable keyword closer to its semantic concept. Automatically assigning relevant text keywords to image is an important problem. Many algorithms and combination of different features have been proposed in the past and achieved good performance. Efforts have focused upon many other fields and some predefined set of features in the area of Automatic image annotation. But properties of features and their complementing combinations have not been well investigated. In this paper the performance of different feature combinations are compared, and find out the one which outperforms the other combinations by applying the Fuzzy K-nearest neighbor algorithm as the classification method.

References
  1. A fuzzy K nearest neighbor algorithm, James M KELLER, Michel R,James A givens,IEEE Transactions on systems,man,and cybernetics,vol,SMU-15,NO:4,JULY/AUGUST 1985.
  2. Content Based Image retrieval using color and texture features. International journal on advanced research in electrical, electronics and instrumentation engineering,vol 1,issue 5,November 2015.
  3. Segmentation and histogram generation using the HSV color space for image retrieval. shamik sural.
  4. Evaluating color descriptors for object scene recognition. Transactions on pattern analysis and machine intelligence, vol 10,no:10, July 2010.
  5. Low level feature extraction of an image for CBIR: Techniques and treands,International journal in advanced electronics engineering,vol 1,issue 1.
  6. The MPEG-7 color descriptors, jens-reiner-ohm, leszek, heon jum kin, santhana krishnamachari
  7. Automatic Image Annotation Using Synthesis of Complementary Features, by Sreekumar k, Anjusha B, Rahul Nair, Department of Computer Science College of Engineering Poonjar Kottayam, Kerala, India
  8. Histogram of oriented gradients for object detection, navneet dalal.
  9. Image texture feature extraction using GLCM approach, p. mohanaiah, p. sathyanaranaiah, l. gurukumar, International journal of scientific and research publications, vol. 3,issue 5,may 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Image Annotation (AIA) Feature Extraction Binary Descriptor color Descriptors Texture Descriptors.