Leave-One-Out Kernel Optimization for Shadow Detection and Removal

The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each region, we train a kernel Least-Squares Support Vector Machine (LSSVM) for separating shadow and non...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2018-03, Vol.40 (3), p.682-695
Main Authors: Vicente, Tomas F. Yago, Minh Hoai, Samaras, Dimitris
Format: Article
Language:English
Subjects:
Classifiers
Histograms
Image detection
Kernel
kernel optimization
Kernels
Labeling
Lighting
Markov processes
Optimization
Shadow detection
shadow removal
Shadows
State of the art
Support vector machines
Training
Training data
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Summary:The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each region, we train a kernel Least-Squares Support Vector Machine (LSSVM) for separating shadow and non-shadow regions. The parameters of the kernel and the classifier are jointly learned to minimize the leave-one-out cross validation error. Optimizing the leave-one-out cross validation error is typically difficult, but it can be done efficiently in our framework. Experiments on two challenging shadow datasets, UCF and UIUC, show that our region classifier outperforms more complex methods. We further enhance the performance of the region classifier by embedding it in a Markov Random Field (MRF) framework and adding pairwise contextual cues. This leads to a method that outperforms the state-of-the-art for shadow detection. In addition we propose a new method for shadow removal based on region relighting. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Once a shadow is detected, we demonstrate that our shadow removal approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2017.2691703