Deep salient object detection by integrating multi-level cues
Date
2017
Authors
Zhang, Jing
Dai, Yuchao
Porikli, Fatih
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Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
A key problem in salient object detection is how to effectively exploit the multi-level saliency cues in a unified and data-driven manner. In this paper, building upon the recent success of deep neural networks, we propose a fully convolutional neural network based approach empowered with multi-level fusion to salient object detection. By integrating saliency cues at different levels through fully convolutional neural networks and multi-level fusion, our approach could effectively exploit both learned semantic cues and higher-order region statistics for edge-Accurate salient object detection. First, we fine-Tune a fully convolutional neural network for semantic segmentation to adapt it to salient object detection to learn a suitable yet coarse perpixel saliency prediction map. This map is often smeared across salient object boundaries since the local receptive fields in the convolutional network apply naturally on both sides of such boundaries. Second, to enhance the resolution of the learned saliency prediction and to incorporate higher-order cues that are omitted by the neural network, we propose a multi-level fusion approach where super-pixel level coherency in saliency is exploited. Our extensive experimental results on various benchmark datasets demonstrate that the proposed method outperforms the state-of the-Art approaches
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Keywords
Semantics, Object detection, Neural networks, Feature extraction, Image segmentation, Image resolution, Machine learning
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Source
Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
Type
Conference paper
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2037-12-31
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