Techniques for efficient and effective transformed image identification
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Author(s)
Awrangjeb, Mohammad
Lu, Guojun
Griffith University Author(s)
Year published
2009
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In many applications, one common problem is to identify images which may have undergone unknown transformations. We define this problem as transformed image identification (TII), where the goal is to identify geometrically transformed and signal processed images for a given test image. The TII consists of three main stages – feature detection, feature representation, and feature matching. The TII approach by Lowe [D.G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision 60 (2) (2004) 91–110] is one of the most promising techniques. However, both of its feature detection and matching stages ...
View more >In many applications, one common problem is to identify images which may have undergone unknown transformations. We define this problem as transformed image identification (TII), where the goal is to identify geometrically transformed and signal processed images for a given test image. The TII consists of three main stages – feature detection, feature representation, and feature matching. The TII approach by Lowe [D.G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision 60 (2) (2004) 91–110] is one of the most promising techniques. However, both of its feature detection and matching stages are expensive, because a large number of feature-points are detected in the image scale-space and each feature-point is described using a high dimensional vector. In this paper, we explore the use of different techniques in each of the three TII stages and propose a number of promising TII approaches by combining different techniques of the three stages. Our experimental results reveal that the proposed approaches not only improve the computational efficiency and decrease the storage requirement significantly, but also increase the transformed image identification accuracy (robustness).
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View more >In many applications, one common problem is to identify images which may have undergone unknown transformations. We define this problem as transformed image identification (TII), where the goal is to identify geometrically transformed and signal processed images for a given test image. The TII consists of three main stages – feature detection, feature representation, and feature matching. The TII approach by Lowe [D.G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision 60 (2) (2004) 91–110] is one of the most promising techniques. However, both of its feature detection and matching stages are expensive, because a large number of feature-points are detected in the image scale-space and each feature-point is described using a high dimensional vector. In this paper, we explore the use of different techniques in each of the three TII stages and propose a number of promising TII approaches by combining different techniques of the three stages. Our experimental results reveal that the proposed approaches not only improve the computational efficiency and decrease the storage requirement significantly, but also increase the transformed image identification accuracy (robustness).
View less >
Journal Title
Journal of Visual Communication and Image Representation
Volume
20
Issue
8
Copyright Statement
© 2009 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Subject
Design
Visual arts
Science & Technology
Technology
Computer Science, Information Systems
Computer Science, Software Engineering
Computer Science