The Secrets of Salient Object Segmentation
Name
CBMM-Memo-014.pdf
Size
1.59 MB
Format
Adobe PDF
Checksum (MD5)
4f707c1aef7ad303393ae7578ede3528
Author(s)
Li, Yin
Hou, Xiaodi
Koch, Christof
Rehg, James M.
Yuille, Alan L.
Date Issued
June 13, 2014
Publisher
Center for Brains, Minds and Machines (CBMM), arXiv
Citation
arXiv:1406.2807v2
Series/Report no.
CBMM Memo Series;014
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 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 xations 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 three existing datasets of segmenting salient objects.
Subjects
Fixation Prediction
Machine Learning
Object Recognition
Terms of Use
Attribution-NonCommercial 3.0 United States
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