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Kernel Based Approaches for Context Based Image Annotatıon

  • L. Swati NairEmail author
  • R. Manjusha
  • Latha Parameswaran
Conference paper
First Online:
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Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

The Exploration of contextual information is very important for any automatic image annotation system. In this work a method based on kernels and keyword propagation technique is proposed. Automatic annotation with a set of keywords for each image is carried out by learning the image semantics. The similarity between the images is calculated by Hellinger’s kernel and Radial Bias Function kernel(RBF)kernel. The images are labelled with multiple keywords using contextual keyword propagation. The results of using the two kernels on the set of features extracted are analysed. The annotation results obtained were validated based on confusion matrix and were found to have a good accuracy. The main advantage of this method is that it can propagate multiple keywords and no definite structure for the annotation keywords has to be considered

Keywords

Automatic image annotation Hellinger’s kernel RBF kernel Semantics Contextual keyword propagation Gabor features Haralick features 
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Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  • L. Swati Nair
    • 1
    Email author
  • R. Manjusha
    • 1
  • Latha Parameswaran
    • 1
  1. 1.Department of Computer Science EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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