Title:
The Secrets of Salient Object Segmentation
The Secrets of Salient Object Segmentation
Author(s)
Li, Yin
Hou, Xiaodi
Koch, Christof
Rehg, James M.
Yuille, Alan L.
Hou, Xiaodi
Koch, Christof
Rehg, James M.
Yuille, Alan L.
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Abstract
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 bench-
marks, 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.
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Date Issued
2014-06
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Text
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Proceedings