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Light Field Saliency Detection with Deep Convolutional Networks
Light field imaging presents an attractive alternative to RGB imaging because
of the recording of the direction of the incoming light. The detection of
salient regions in a light field image benefits from the additional modeling of
angular patterns. For RGB imaging, methods using CNNs have achieved excellent
results on a range of tasks, including saliency detection. However, it is not
trivial to use CNN-based methods for saliency detection on light field images
because these methods are not specifically designed for processing light field
inputs. In addition, current light field datasets are not sufficiently large to
train CNNs. To overcome these issues, we present a new Lytro Illum dataset,
which contains 640 light fields and their corresponding ground-truth saliency
maps. Compared to current light field saliency datasets [1], [2], our new
dataset is larger, of higher quality, contains more variation and more types of
light field inputs. This makes our dataset suitable for training deeper
networks and benchmarking. Furthermore, we propose a novel end-to-end CNN-based
framework for light field saliency detection. Specifically, we propose three
novel MAC (Model Angular Changes) blocks to process light field micro-lens
images. We systematically study the impact of different architecture variants
and compare light field saliency with regular 2D saliency. Our extensive
comparisons indicate that our novel network significantly outperforms
state-of-the-art methods on the proposed dataset and has desired generalization
abilities on other existing datasets.Comment: 14 pages, 14 figure
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