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Framework for Content-Based Image Identification with Standardized Multiview Features

  • Das, Rik (Department of Information Technology, Xavier Institute of Social Service) ;
  • Thepade, Sudeep (Department of Information Technology, Pimpri Chinchwad College of Engineering) ;
  • Ghosh, Saurav (Department of Information Technology, A.K. Choudhury School of Information Technology)
  • Received : 2015.02.03
  • Accepted : 2015.06.25
  • Published : 2016.02.01

Abstract

Information identification with image data by means of low-level visual features has evolved as a challenging research domain. Conventional text-based mapping of image data has been gradually replaced by content-based techniques of image identification. Feature extraction from image content plays a crucial role in facilitating content-based detection processes. In this paper, the authors have proposed four different techniques for multiview feature extraction from images. The efficiency of extracted feature vectors for content-based image classification and retrieval is evaluated by means of fusion-based and data standardization-based techniques. It is observed that the latter surpasses the former. The proposed methods outclass state-of-the-art techniques for content-based image identification and show an average increase in precision of 17.71% and 22.78% for classification and retrieval, respectively. Three public datasets - Wang; Oliva and Torralba (OT-Scene); and Corel - are used for verification purposes. The research findings are statistically validated by conducting a paired t-test.

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