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Title: Pixel-wise ordinal classification for salient object grading
Authors: Liu, Yanzhu
Wang, Yanan
Kong, Adams Wai Kin
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Liu, Y., Wang, Y. & Kong, A. W. K. (2021). Pixel-wise ordinal classification for salient object grading. Image and Vision Computing, 106, 104086-.
Project: MOE2016-T2-1-042(S)
Journal: Image and Vision Computing
Abstract: Driven by business intelligence applications for rating attraction of products in shops, a new problem — salient object grading is studied in this paper. In computer vision, plenty of salient object detection approaches have been proposed, while most existing studies detect objects in a binary manner: salient or not. This paper focuses on a new problem setting that requires detecting all salient objects and categorizing them into different salient levels. Based on that, a pixel-wise ordinal classification method is proposed. It consists of a multi-resolution saliency detector which detects and segments objects, an ordinal classifier which grades pixels into different salient levels, and a binary saliency enhancer which sharpens the difference between non-saliency and all other salient levels. Two new image datasets with salient level labels are constructed. Experimental results demonstrate that, on the one hand, the proposed method provides effective salient level predictions and on the other hand, offers very comparable performance with state-of-the-art salient object detection methods in the traditional problem setting.
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2020.104086
Schools: School of Computer Science and Engineering 
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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