The Secrets of Salient Object Segmentation

In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient object benchmarks, called the dataset design bias, by over emphasising the ster...

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Vydáno v:2014 IEEE Conference on Computer Vision and Pattern Recognition s. 280 - 287
Hlavní autoři: Yin Li, Xiaodi Hou, Koch, Christof, Rehg, James M., Yuille, Alan L.
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.06.2014
Témata:
Algorithm design and analysis
Algorithms
Benchmark testing
Benchmarks
Bias
Computer vision
dataset analysis
Design engineering
eye fixation
Fixation
Image color analysis
Image segmentation
Labeling
Object segmentation
Pattern recognition
Prediction algorithms
Saliency
salient object segmentation
Segmentation
Statistics
ISSN:1063-6919, 1063-6919
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Shrnutí:In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient object benchmarks, called the dataset design bias, by over emphasising the stereotypical concepts of saliency. The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing. Based on our analysis, we propose a new high quality dataset that offers both fixation and salient object segmentation ground-truth. With fixations and salient object being presented simultaneously, we are able to bridge the gap between fixations and salient objects, and propose a novel method for salient object segmentation. Finally, we report significant benchmark progress on 3 existing datasets of segmenting salient objects.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.43