Abstract— The exponential growth in image data over the internet has resulted in a growing need for searching images according to our requirements. Content based image retrieval systems extract similar images from databases or the internet for facilitation of their users. A number of different feature sets and classifiers have been used by researchers for content based image retrieval. The goal of this research is to evaluate some common features sets used for classification of images and identify the best features depending upon the user requirement. Some commonly used features have been studies and a set of six feature sets have been selected for evaluation by the Back-Propagation Neural Network (BPNN). The results have been evaluated on the basis of precision and recall and it can be concluded that for natural images none of the feature sets perform well universally on all classes and the selection of optimal feature set depends on the type/class of images.
Index Terms— Back-propagation neural network, content based image retrieval, feature extraction, image analysis, pattern recognition.
Syed A. Husain is with King Faisal University, Kingdom of Saudi Arabia (e-mail: sahusain@kfu.edu.sav).
Fauzia S. Akbar is with Riphah International University, Pakistan (e-mail: fouzia_sher_akbar@yahoo.com).
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Cite: Syed A. Husain and Fouzia S. Akbar, " A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network," International Journal of Information and Electronics Engineering vol. 5, no. 1, pp. 1-5, 2015.
