Open AccessSalient object detection based on meanshift filtering and fusion of colour information
Author(s) -
Li Jian,
Chen Haifeng,
Li Gang,
He Bin,
Zhang Yujie,
Tao Xiaojiao
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0803
Subject(s) - robustness (evolution) , computer science , artificial intelligence , salient , pattern recognition (psychology) , segmentation , pixel , fusion , domain (mathematical analysis) , computer vision , spatial analysis , object (grammar) , image (mathematics) , object detection , mathematics , statistics , mathematical analysis , biochemistry , chemistry , linguistics , philosophy , gene
Colour and its spatial distribution are the main information currently used to detect salient objects in an image, but this cannot always guarantee satisfying performance. To deal with this problem, a salient object detection algorithm has been presented based on meanshift filtering and fusion of colour information. Superpixel segmentation is used to analyse the images by sets of pixels instead of single pixel, which improves the robustness of the algorithm to noises, as well as the efficiency. Meanshift filtering is used to detect the modes of every superpixel in spatial domain and range domain, respectively, which is the basis of the subsequent calculation. Each target therefore offers almost the same saliency and the spatial distribution of which will be easier to analyse. The fusion of colour contrast and colour concentration as well as centre prior is used as criterion to evaluate the saliency of every single superpixel. According to the tests of the algorithm on the open popular dataset, it has been proved that the algorithm presented in this work shows better results in both the aspect of effectiveness and efficiency, compared with its traditional equivalents.