Open AccessSalient object detection via reliability‐based depth compactness and depth contrast
Author(s) -
Zhou Yang,
Liu Xiaoqi,
Zhang Yun,
Yin Haibing,
Lu Yu
Publication year - 2020
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.2019.1495
Subject(s) - salient , artificial intelligence , feature (linguistics) , computer science , contrast (vision) , computer vision , depth map , reliability (semiconductor) , pixel , stereoscopy , pattern recognition (psychology) , image (mathematics) , feature extraction , philosophy , linguistics , power (physics) , physics , quantum mechanics
It can be intuitively inferred that a high‐quality depth map can be used to quickly detect the salient region in stereo vision, implying that depth information plays an essential role in stereoscopic visual attention. However, existing methods generally use the depth map as an auxiliary cue to improve the saliency detection performance. In this study, the authors present an algorithm to directly detect the salient object from a high‐quality depth image. The proposed algorithm utilises a depth reliability indicator to assess the confidence of a depth image. Depth compactness, a novel feature that incorporates the depth reliability of the super‐pixels, is computed as a primary salient feature. Moreover, in order to enhance another salient feature (i.e. depth contrast), they develop a coarse background filtering method to suppress background interference. Experimental results demonstrate that the proposed method performs favourably against the popular depth‐aware saliency detection approaches at a lower computational cost.