RGBD Image Co-saliency Object Detection Based on Sample Selection

Zhengyi LIU; Junlei LIU; Peng ZHAO

doi:10.11999/JEIT190393

Volume 42 Issue 9

Sep. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(9): 2277-2284

Zhengyi LIU, Junlei LIU, Peng ZHAO. RGBD Image Co-saliency Object Detection Based on Sample Selection[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2277-2284. doi: 10.11999/JEIT190393

Citation:

Zhengyi LIU, Junlei LIU, Peng ZHAO. RGBD Image Co-saliency Object Detection Based on Sample Selection[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2277-2284. doi: 10.11999/JEIT190393

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RGBD Image Co-saliency Object Detection Based on Sample Selection

doi: 10.11999/JEIT190393

School of Computer Science and Technology, Anhui University, Hefei 230601, China

Funds: The Provincial Natural Science Foundation of Anhui(1908085MF182), The National Natural Science Foundation of China(61602004)

Received Date: 2019-06-03
Rev Recd Date: 2020-03-01

Available Online: 2020-06-27

Publish Date: 2020-09-27

Abstract

Abstract

Co-saliency object detection aims to discover common and salient objects in an image group which contains two or more relevant images. In this paper, a method of using machine learning is proposed to detect co-saliency objects. Firstly, some simple images are selected to form a simple image set based on four scoring indicators. Secondly, positive and negative samples are extracted from the simple images set based on co-coherence characteristics, and high-dimensional semantic features are extracted by the deep learning model which receives RGBD four-channels input. Thirdly, the co-saliency classifier is trained by positive and negative samples, and co-saliency maps are generated by testing all the superpixels in the images by the co-saliency classifier. Finally, a smooth fusion operation is adopted to generate the final co-saliency map. Experimental results on the public benchmark dataset show that the proposed algorithm is superior to the state-of-the-art methods in terms of accuracy and efficiency, and it is robust.
- Object detection,
- Co-saliency object,
- RGBD images,
- Deep learning,
- Classifier

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References

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