Semisupervised learning based on a novel iterative optimization model for saliency detection

Huo, Shuwei, Zhou, Yuan, Xiang, Wei, and Kung, Sun-Yuan (2019) Semisupervised learning based on a novel iterative optimization model for saliency detection. IEEE Transactions on Neural Networks and Learning Systems, 30 (1). pp. 225-241.

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Abstract

In this paper, we propose a novel iterative optimization model for bottom-up saliency detection. By exploring bottom-up saliency principles and semisupervised learning approaches, we design a high-performance saliency analysis method for wide ranging scenes. The proposed algorithm consists of two stages: 1) we develop a boundary homogeneity model to characterize the general position and the contour of the salient objects and 2) we propose a novel iterative optimization model, termed gradual saliency optimization, for further performance improvement. Our main contribution falls on the second stage, where we propose an iterative framework with self-repairing mechanisms for refining saliency maps. In this framework, we further develop a more comprehensive optimization function applying a novel semisupervised learning scheme to enhance the traditional saliency measure. More elaborately, the iterative method can gradually improve the output in each iteration and finally converge to high-quality saliency maps. Based on our experiments on four different public data sets, it can be demonstrated that our approach significantly outperforms the state-of-the-art methods.

Item ID: 56879
Item Type: Article (Research - C1)
ISSN: 2162-2388
Keywords: iterative optimization, saliency detection, saliency map refinement, semi-supervised learning
Copyright Information: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Funders: National Natural Science Foundation of China (NSFC), Natural Science Foundation of Tianjin, China (NSFT)
Projects and Grants: NSFC grant 61571326, NSFC grant 61520106002, NSFT grant 16JCQNJC00900
Date Deposited: 16 Jan 2019 07:39
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100%
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