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Proceedings Paper

Saliency detection via background features
Author(s): Wei Jiang; Houde Dai; Yadan Zeng; Mingqiang Lin
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Paper Abstract

The background information is full of essential clues which could be used to distinguish the foreground from images, especially when images contain multiple targets or complex backgrounds. In this paper, we formulate the saliency detection task as a labelling problem. We propose a novel saliency detection method via fusing a set of features based on background information. We firstly extract background features referred to as uniqueness feature, dense feature, and sparse feature. Specifically, uniqueness feature is defined using the color distinction and spatial distance based on the K-means algorithm; dense feature of the background segments is calculated by the PCA algorithm; sparse feature is computed based on the sparse encode algorithm. Then we fuse these background features under the CRF frame. Finally, we evaluate our proposed method on a new constructed dataset from THUS10000, SOD and ECSSD datasets to cover different scenarios. The experimental results show that our method can be well against the previous methods in terms of precision and recall.

Paper Details

Date Published: 9 August 2018
PDF: 10 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060T (9 August 2018); doi: 10.1117/12.2503198
Show Author Affiliations
Wei Jiang, Quanzhou Institute of Equipment Manufacturing (China)
Houde Dai, Quanzhou Institute of Equipment Manufacturing (China)
Yadan Zeng, Quanzhou Institute of Equipment Manufacturing (China)
Mingqiang Lin, Quanzhou Institute of Equipment Manufacturing (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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