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Semantic Priors for Intrinsic Image DecompositionAuthors: Saurabh Saini,P J Narayanan Conference: British Machine Vision Conference (BMVC) (BMVC-2018 2018) Location New Castle, UK Date: 2018-09-03 Report no: IIIT/TR/2018/134 AbstractIntrinsic Image Decomposition (IID) is a challenging and interesting computer vision problem with various applications in several fields. We present novel semantic priors and an integrated approach for single image IID that involves analyzing image at three hierarchical context levels. Local context priors capture scene properties at each pixel within a small neighborhood. Mid-level context priors encode object level semantics. Global context priors establish correspondences at the scene level. Our semantic priors are designed on both fixed and flexible regions, using selective search method and Convolutional Neural Network features. Experiments and analysis of our method indicate the utility of our weak semantic priors and structured hierarchical analysis in an IID framework. We compare our method with the current state-of-the-art and show results with lesser artifacts. Finally, we highlight that proper choice and encoding of prior knowledge can produce competitive results compared to end-to-end deep learning IID methods, signifying the importance of such priors. We believe that the insights and techniques presented in this paper would be useful in the future IID research. Full paper: pdf Centre for Visual Information Technology |
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