Open AccessRobust saliency detection via corner information and an energy function
Author(s) -
Zhang Hanling,
Xia Chenxing,
Gao Xiuju
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0492
Subject(s) - artificial intelligence , computer science , saliency map , image (mathematics) , ranking (information retrieval) , computer vision , pattern recognition (psychology) , construct (python library) , relevance (law) , filter (signal processing) , convex hull , energy (signal processing) , function (biology) , regular polygon , mathematics , statistics , geometry , evolutionary biology , political science , law , biology , programming language
In this study, the authors propose a distinctive bottom‐up visual saliency detection algorithm based on a new background prior and a new reinforcement. Inspired by genetic algorithm, the final map is obtained with three steps. First of all, the authors construct a background‐based saliency map by manifold ranking via superior image corners selected by convex‐hull as background prior, which is different from most of the existing background prior‐based methods treated all image boundaries as background. Then, a better result is obtained by ranking the relevance of the image elements with foreground seeds extracted from the preliminary saliency map. Furthermore, a novel optimisation framework is introduced with the intention of refining the map, which integrates an energy function with a guided filter. Experimental results on three public datasets indicate that the proposed method performs favourably against the state‐of‐the‐art algorithms.