Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6938297 | Journal of Visual Communication and Image Representation | 2018 | 9 Pages |
Abstract
State-of-the-art techniques for Camera Model Identification operate by extracting different features from the training image set and incorporating those features to predict the source of test images using machine learning. Though the existing approaches perform efficiently for images captured in natural daylight or bright illumination conditions, the state-of-the-art lacks sufficient experiments and results to evaluate efficiency of such schemes for images captured in dark illumination conditions. In this paper, we present a set of experiments to assess the impact of illumination conditions, on image source classification problem, and also propose an image filtering based technique to eliminate the adverse effects of scene illumination on source classification accuracy. Our experimental results prove that the performance efficiency of existing feature based source classification techniques, is indeed dependent on the illumination conditions. The proposed strategy enables our source classification model to achieve high efficiency as compared to the state-of-the-art, under all illumination conditions.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Udaya Sameer Venkata, Ruchira Naskar,