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Type: Artigo de periódico
Title: Progressive randomization: Seeing the unseen
Author: Rocha, A
Goldenstein, S
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.
Subject: Image inference
Progressive randomization
Image categorization
Image forensics
Country: EUA
Editor: Academic Press Inc Elsevier Science
Citation: Computer Vision And Image Understanding. Academic Press Inc Elsevier Science, v. 114, n. 3, n. 349, n. 362, 2010.
Rights: fechado
Identifier DOI: 10.1016/j.cviu.2009.10.002
Date Issue: 2010
Appears in Collections:Unicamp - Artigos e Outros Documentos

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