Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100801
Title: Saliency density maximization for efficient visual objects discovery
Authors: Luo, Ye
Yuan, Junsong
Xue, Ping
Tian, Qi
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Source: Luo, Y., Yuan, J., Xue, P., & Tian, Q. (2011). Saliency density maximization for efficient visual objects discovery. IEEE transactions on circuits and systems for video technology, 21(12), 1822-1834.
Series/Report no.: IEEE transactions on circuits and systems for video technology
Abstract: Detection of salient objects in an image remains a challenging problem despite extensive studies in visual saliency, as the generated saliency map is usually noisy and incomplete. In this paper, we propose a new method to discover the salient object without prior knowledge on its shape and size. By searching the sub-image, i.e., a bounding box of maximum saliency density, the new formulation can automatically crop the salient objects of various sizes in spite of the cluttered background, and is capable to handle different types of saliency maps. A global optimal solution is obtained by the proposed density-based branch-and-bound search. The proposed method can apply to both images and videos. Experimental results on a public dataset of five thousand images show that our unsupervised detection approach is comparable to the state-of-the-art learning based methods. Promising results are also observed in the salient object detection for videos with a good potential in video retargeting.
URI: https://hdl.handle.net/10356/100801
http://hdl.handle.net/10220/18222
ISSN: 1051-8215
DOI: 10.1109/TCSVT.2011.2147230
Rights: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: http://dx.doi.org/10.1109/TCSVT.2011.2147230.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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