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RETRACTED ARTICLE: Recurrent learning of context for salient region detection

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This article was retracted on 04 September 2023

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Abstract

In this paper, a novel type of saliency region detection method is proposed based on the recurrent learning of context. It aims to find the image regions that can represent the main content. It is different with previous definitions the goal of which is to either find fixation points or seek the dominant object. The regions should own semantic information, thus being a challenging task for computer vision, especially when the imaging quality is poor with complicated background clutter and uncontrolled viewing conditions. To improve attribute recognition given small-sized training data with poor-quality images, we formulate a joint recurrent learning model for exploring context and correlation, based on which salient region can be detected. Moreover, by the way of incorporating semantic information of image contents, an object oriented pooling strategy is proposed to further improve the performance. We conduct experiments on several challenging publically available saliency detection datasets and it demonstrates the effectiveness of our proposed saliency region detection method.

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Authors and Affiliations

  1. College of Artificial Intelligenge and Big Data, ChongQing University of Electronic Engineering, Chongqing, 401331, China

    Chunling Wu

  2. Institute of Education, Xiamen University, Xiamen Shi, 361005, China

    Chunling Wu

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  1. Chunling Wu
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Correspondence to Chunling Wu.

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Received the B.S. degrees at the School of Computer Science, Chongqing University,Chongqing, China, in 2002, and the M.S. degree in software engineering from School of Software Engineering at Chongqing University, Chongqing, China, in 2005. His research interests include information security, cloud computing, and artificial intelligence.

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Wu, C. RETRACTED ARTICLE: Recurrent learning of context for salient region detection. Pers Ubiquit Comput 22, 1017–1027 (2018). https://doi.org/10.1007/s00779-018-1171-0

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  • DOI: https://doi.org/10.1007/s00779-018-1171-0

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