Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17851
Title: Visual-Patch-Attention-Aware Saliency Detection
Authors: Jian, M
Lam, KM 
Dong, J
Shen, L
Keywords: Directional patches
Human visual system (HVS)
Meaningful patches
Salient region detection
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2015, v. 45, no. 8, p. 1575-1586 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: The human visual system (HVS) can reliably perceive salient objects in an image, but, it remains a challenge to computationally model the process of detecting salient objects without prior knowledge of the image contents. This paper proposes a visual-attention-aware model to mimic the HVS for salient-object detection. The informative and directional patches can be seen as visual stimuli, and used as neuronal cues for humans to interpret and detect salient objects. In order to simulate this process, two typical patches are extracted individually and in parallel from the intensity channel and the discriminant color channel, respectively, as the primitives. In our algorithm, an improved wavelet-based salient-patch detector is used to extract the visually informative patches. In addition, as humans are sensitive to orientation features, and as directional patches are reliable cues, we also propose a method for extracting directional patches. These two different types of patches are then combined to form the most important patches, which are called preferential patches and are considered as the visual stimuli applied to the HVS for salient-object detection. Compared with the state-of-the-art methods for salient-object detection, experimental results using publicly available datasets show that our produced algorithm is reliable and effective.
URI: http://hdl.handle.net/10397/17851
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2014.2356200
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