Global-Context Based Salient Region Detection in Nature Images

• Published in: IEICE Transactions on Information and Systems Vol. E95.D; no. 5; pp. 1556 - 1559
• Main Authors: BAO, Hong, XU, De, TANG, Yingjun
• Format: Journal Article
• Language: English; Japanese
• Published: Oxford The Institute of Electronics, Information and Communication Engineers 2012; Oxford University Press
• Subjects: Applied sciences; Artificial intelligence; Background noise; Computer science; control theory; systems; Computer vision; Exact sciences and technology; global color context; Image processing; Information theory; Information, signal and communications theory; Mathematical models; matrix decomposition; Methodology; Noise; Pattern recognition. Digital image processing. Computational geometry; salient map; Segments; Signal processing; Telecommunications and information theory; Visual; visual attention; Performance evaluation; Computer vision; Content based retrieval; Background; global color context; Image retrieval; Information retrieval; salient map; Matrix decomposition; Visual attention; Implementation
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.E95.D.1556

Visually saliency detection provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. One of the main aims of visual attention in computer vision is to detect and segment the salient regions in an image. In this paper, we employ matrix decomposition to detect salient object in nature images. To efficiently eliminate high contrast noise regions in the background, we integrate global context information into saliency detection. Therefore, the most salient region can be easily selected as the one which is globally most isolated. The proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. Experiments show that our approach achieves much better performance than that from the existing state-of-art methods.