Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 24, Issue 12, Pages 5671-5683Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2485782
Keywords
Salient object detection; normalized cut; clustering; region merging; saliency map
Funding
- National Natural Science Foundation, China [61273258]
- 863 Plan, China [2015AA042308]
Ask authors/readers for more resources
Existing salient object detection models favor over-segmented regions upon which saliency is computed. Such local regions are less effective on representing object holistically and degrade emphasis of entire salient objects. As a result, the existing methods often fail to highlight an entire object in complex background. Toward better grouping of objects and background, in this paper, we consider graph cut, more specifically, the normalized graph cut (Ncut) for saliency detection. Since the Ncut partitions a graph in a normalized energy minimization fashion, resulting eigenvectors of the Ncut contain good cluster information that may group visual contents. Motivated by this, we directly induce saliency maps via eigenvectors of the Ncut, contributing to accurate saliency estimation of visual clusters. We implement the Ncut on a graph derived from a moderate number of superpixels. This graph captures both intrinsic color and edge information of image data. Starting from the superpixels, an adaptive multi-level region merging scheme is employed to seek such cluster information from Ncut eigenvectors. With developed saliency measures for each merged region, encouraging performance is obtained after across-level integration. Experiments by comparing with 13 existing methods on four benchmark datasets, including MSRA-1000, SOD, SED, and CSSD show the proposed method, Ncut saliency, results in uniform object enhancement and achieves comparable/better performance to the state-of-the-art methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available