Salient Object Detection with Complex Scene Based on Cognitive Neuroscience

Detecting salient objects with complex backgrounds is still a challenging problem. Under the background having similar colors with complex patterns of salient objects, existing methods' performance is not satisfied, especially for multiple salient objects detection. In this paper, we propose a...

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Bibliographic Details
Published in2017 IEEE Third International Conference on Multimedia Big Data (BigMM) pp. 33 - 37
Main Authors Chunbiao Zhu, Ge Li, Wenmin Wang, Ronggang Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Center-bias
Depth information
Euclidean distance
Feature extraction
Image color analysis
Luminance map
Neuroscience
Object detection
Salient object detection
Visual systems
Visualization
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Summary:Detecting salient objects with complex backgrounds is still a challenging problem. Under the background having similar colors with complex patterns of salient objects, existing methods' performance is not satisfied, especially for multiple salient objects detection. In this paper, we propose a framework based on cognitive neuroscience to tackle with these challenges. According to cognitive neuroscience, human visual system is sensitive to depth of field, conspicuous color, moving objects and central object of scene. In the proposed framework, we imitate these human visual characteristics with following approaches: (1) using depth to represent the depth of field in the real world, (2) using luminance which imitates the light changing to represent the relative motions among objects, (3) using the center-bias to enhance object around the center. Experimental results on two challenging RGB-D datasets demonstrate that our method is superior to the existing methods in terms of effectiveness.
DOI:10.1109/BigMM.2017.22