Open AccessSaliency detection using suitable variant of local and global consistency
Author(s) -
Chen Jiazhong,
Chen Jie,
Cao Hua,
Li Rong,
Xia Tao,
Ling Hefei,
Chen Yang
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.2016.0453
Subject(s) - artificial intelligence , computer science , contrast (vision) , consistency (knowledge bases) , fuse (electrical) , pattern recognition (psychology) , image (mathematics) , function (biology) , saliency map , salient , kadir–brady saliency detector , matrix (chemical analysis) , computer vision , factor (programming language) , engineering , materials science , composite material , evolutionary biology , electrical engineering , biology , programming language
In existing local and global consistency (LGC) framework, the cost functions related to classifying functions adopt the sum of each row of weight matrix as an important factor. Some of these classifying functions are successfully applied to saliency detection. From the point of saliency detection, this factor is inversely proportional to the colour contrast between image regions and their surroundings. However, an image region that holds a big colour contrast against it surroundings does not denote it must be a salient region. Therefore a suitable variant of LGC is introduced by removing this factor in cost function, and a suitable classifying function (SCF) is decided. Then a saliency detection method that utilises the SCF, content‐based initial label assignment scheme, and appearance‐based label assignment scheme is presented. Via updating the content‐based initial labels and appearance‐based labels by the SCF, a coarse saliency map and several intermediate saliency maps are obtained. Furthermore, to enhance the detection accuracy, a novel optimisation function is presented to fuse the intermediate saliency maps that have a high detection performance for final saliency generation. Numerous experimental results demonstrate that the proposed method achieves competitive performance against some recent state‐of‐the‐art algorithms for saliency detection.