Intrinsic 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 neighbourhood. 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. Our IID method is an iterative multistage optimization scheme and consists of two complementary formulations: @math smoothing for shading and @math sparsity for reflectance. Experiments and analysis of our method indicate the utility of our semantic priors and structured hierarchical analysis in an IID framework. We compare our method with other contemporary IID solutions and show results with lesser artifacts. Finally, we highlight that proper choice and encoding of prior knowledge can produce competitive results even when 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.
In our model, we absorb the advantages of both the supervised and unsupervised approaches by combining the generality of supervised deep learning methods with prior domain knowledge. We employ an off-the-shelf' pre-trained deep neural network as a black-box to obtain generic features. Additionally we also use an unsupervised method to provide yet another set of semantic features. We use both these features to introduce new context priors in an unsupervised optimization algorithm by posing it as a standard total variation optimization problem @cite_23 . Recently great improvements have been made in IID with the use of supervised methods primarily utilizing new large new training datasets such as: time lapse dataset , multi-illumination dataset and synthetic datasets . Bigger datasets improve the performance of supervised learning systems, but we can improve these solutions by explicitly encoding problem specific insights. We believe our IID specific semantic priors, encode crucial domain insights and would further help such methods.
@cite_23
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The class of L1-regularized optimization problems has received much attention recently because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. Despite this recent attention, many L1-regularized problems still remain difficult to solve, or require techniques that are very problem-specific. In this paper, we show that Bregman iteration can be used to solve a wide variety of constrained optimization problems. Using this technique, we propose a “split Bregman” method, which can solve a very broad class of L1-regularized problems. We apply this technique to the Rudin-Osher-Fatemi functional for image denoising and to a compressed sensing problem that arises in magnetic resonance imaging.