Framework for Content-Based Image Identification with Standardized Multiview Features
Rik Kamal Kumar Das, Sudeep D. Thepade, Saurav Ghosh · ETRI Journal · 2016
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.