| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Estadual Campinas, Inst Comp, BR-13084851 Campinas, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | COMPUTER VISION AND IMAGE UNDERSTANDING; v. 114, n. 3, p. 349-362, MAR 2010. |
| Web of Science Citations: | 7 |
| Abstract | |
In this paper, we introduce the progressive randomization (PR): a new image meta-description approach suitable for different image inference applications such as broad class Image Categorization Forensics and, Steganalysis. The main difference among PR and the state-of-the-art algorithms is that it is based on progressive perturbations on pixel values of images. With such perturbations, PR captures the image class separability allowing us to successfully infer high-level information about images. Even when only a limited number of training examples are available, the method still achieves good separability, and its accuracy increases with the size of the training set. We validate the method using two different inference scenarios and four image databases. (C) 2009 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 07/52015-0 - Approximation methods for visual computing |
| Grantee: | Jorge Stolfi |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 08/08681-9 - Digital Image Forensics: Forgery and spoofing detection |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 05/58103-3 - Classifiers and machine learning techniques for image processing and computer vision |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Scholarships in Brazil - Doctorate |