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Efficient Automatic Image Annotation using Weighted Feature Fusion and its Optimization using Genetic Algorithm

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Ajimi Ameer, Sree Kumar. K Published in Image Processing

Efficient Automatic Image Annotation using Weighted Feature Fusion and its Optimization using Genetic Algorithm
Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal
Series Volume 1 Number 6
Authors Ajimi Ameer, Sree Kumar. K
10.5120/cae-1567
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Ajimi Ameer and Sree Kumar.k. Article: Efficient Automatic Image Annotation using Weighted Feature Fusion and its Optimization using Genetic Algorithm. Communications on Applied Electronics 1(6):15-19, April 2015. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Ajimi Ameer and Sree Kumar.k},
	title = {Article: Efficient Automatic Image Annotation using Weighted Feature Fusion and its Optimization using Genetic Algorithm},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {1},
	number = {6},
	pages = {15-19},
	month = {April},
	note = {Published by Foundation of Computer Science, New York, USA}
}

Abstract

The conventional way of text based retrieval systems are now being replaced by the visual content based systems in image retrieval. The image content has several dominant characteristics features like texture,color,shape and it is interesting to research the classification of images on content-basis with these features. These different descriptor can be combined to form a single feature vector. However, in order to get optimum performance and to reduce the feature dimensionality for making system close to human perception, genetic algorithm (GA) based feature selection is used in this paper. Single feature describes image content only from one point of view, which will not give a correct result. Fusing multi- feature similarity score is expected to improve the accuracy of system's retrieval performance. Inorder to assign the fusion weights of multi-feature similarity scores reasonably according to an image, the genetic algorithm is applied.

Reference

  1. Chandrashekhar G. Patil, Dr. Mahesh T. Kolte2, Dr. Devendra S. Chaudhari, Fusion at Features Level in CBIR System using Genetic Algorithm,2013
  2. Sapthagiri. k, Manickam , An Efficient Image Retrieval Based on Color, Texture (GLCM & CCM) ,features, and Genetic-Algorithm,2013
  3. K. APARNA, Retrieval of Digital Images Based On Multi-Feature Similarity Using Genetic Algorithm, 2013
  4. Ruaa Mohammed Hamza, Dr. Tawfiq A. Al-Assadi, Genetic Algorithm to find optimal GLCM features,2012.
  5. Shafimirza,Dr. J. Apparao, Retrieval Of Digital Images Using Texture Feature With Advanced Genetic Algorithm,2012
  6. S. Sreenivas Rao , Mr. K. Ravi Kumar, Dr. G. Lavanya Devi, Texture Based Image retrieval using Human interactive Genetic Algorithm,2013
  7. S. Gopalakrishnan, Dr. P. Aruna, Retrieval of images based on low level features using genetic algorithm,2014
  8. K. Kalaiyarasi ,A. Kabilar M. Image Retreival Based On Colour, Texture and Shape Analysis Using Genetic Algorithm,2014
  9. P. Kishore Kumar, M. Radhika, Using Genetic Algorithm Image Retrieval Based on Multi- Feature Similarity Score Fusion,2014
  10. Dr. Mahesh. T. Kolte, Chandrashekhar, Improvement in Performance of CBIR by using fusion and Evolutionary Computation,2014
  11. Anita Nanasaheb Ligade, Manisha R. Patil, Optimized content based image retrieval using genetic algorithm with relevance feedback technique,2013
  12. Miguel Arevalillo-Herraez, Francesc J. Ferri, Salvador Moreno-Picot, An interactive evolutionary approach for content based image retrieval,2013
  13. M. Venkat Dass, Mohammed Rahmath Ali, Mohammed Mahmood Ali, Image Retrieval Using Interactive Genetic Algorithm,2014
  14. Ni-Bin Chang and Benjamin Vannah, Compartive Data Fusion between Genetic Programing and Nueral Network Models for Remote Sensing Images of Water QualityMonitoring, 2014
  15. Itedal Sabri Hashim Bahia, Using Artificial Neural Network Modeling in Forecasting Revenue: Case Study in National Insurance Company/Iraq, 2013
  16. Joey Mark Diaz, Raymond Christopher Pinon, Geoffrey Solano, Lung Cancer Classification Using Genetic Algorithm to Optimize Prediction Models,2013
  17. Ms. N. T. Renukadevi and Dr. P. Thangaraj , Performance analysis of optimization techniques for medical image retrieval,2014
  18. Miguel Arevalillo-Herr´aez, Francesc J. Ferri, Salvador Moreno-Picot, An interactive evolutionary approach for content based image retrieval,2013
  19. Song, X. N. ; Zhao, Y. S. Study on component temperatures inversion using satellite remotely sensed data. Int. J. Remote Sens. 2007.
  20. C. Ramesh babu durai, V. Duraisamy, C. Vinothkumar,Improved Content Based Image Retrieval Using Neural Network Optimization with Genetic Algorithm 2012
  21. AshokSamala,,SanjivBhatiab,PrasanthVadlamania, DavidMarxc, Searching satellite imagery with integrated measures,2009
  22. Ji Zhou , Xu Zhang , Wenfeng Zhan and Huailan Zhang ,Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region ,2014.
  23. Dengsheng Zhang, Aylwin Wong, Maria Indrawan, Guojun Lu, Content-based Image Retrieval Using Gabor Texture Features.
  24. Chandrika L, Implementation Image Retrieval and Classification with SURF Technique, 2014
  25. MS. R. Janani,Sebhakumar. P ,An Improved CBIR Method Using Color and Texture Properties with Relevance Feedback, 2014.

Keywords

Genetic Algorithm,Feature vector weights ,Feature fusion, HOG,SURF,HSV