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An adaptive framework for saliency detection
Author(s) -
Jia Ning,
Liu Xianhui,
Zhao Weidong,
Zhang Haotian,
Zhuo Keqiang
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22351
Subject(s) - artificial intelligence , pixel , computer science , thresholding , image (mathematics) , benchmark (surveying) , computer vision , pattern recognition (psychology) , salient , binary image , context (archaeology) , binary number , set (abstract data type) , image processing , mathematics , geography , arithmetic , geodesy , archaeology , programming language
At present, people are inclined to use one saliency detection method to cover all the pixels in an image. However, every method has its own limitations. A single method may not yield a good performance at all image scenes. In this article, we propose a new adaptive framework to detect salient objects. For each pixel in an image, it adaptively selects an appropriate method according to the pixel context relationship. In our framework, an image is characterized by a set of binary maps, which are generated by randomly thresholding the image's initial saliency map. And then, we utilize the surroundedness cue, which are obtained by a series of operations on the binary maps, to classify all the pixels in an image. Furthermore, based on the classes, we choose methods to detect salient objects. Extensive experimental results on three benchmark datasets demonstrate that our method performs favorable against 11 state‐of‐the‐art methods.