Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81409
Title: Saliency density maximization for object detection and localization
Authors: Luo, Ye
Yuan, Junsong
Xue, Ping
Tian, Qi
Keywords: Electrical and Electronic Engineering
Issue Date: 2011
Source: Luo, Y., Yuan, J., Xue, P., & Tian, Q. (2011) Saliency Density Maximization for Object Detection and Localization. Proceedings of the 10th Asian Conference on Computer Vision (ACCV 2010), 396-408.
Abstract: Accurate localization of the salient object from an image is a difficult problem when the saliency map is noisy and incomplete. A fast approach to detect salient objects from images is proposed in this paper. To well balance the size of the object and the saliency it contains, the salient object detection is first formulated with the maximum saliency density on the saliency map. To obtain the global optimal solution, a branch-and-bound search algorithm is developed to speed up the detection process. Without any prior knowledge provided, the proposed method can effectively and efficiently detect salient objects from images. Extensive results on different types of saliency maps with a public dataset of five thousand images show the advantages of our approach as compared to some state-of-the-art methods.
URI: https://hdl.handle.net/10356/81409
http://hdl.handle.net/10220/18134
DOI: 10.1007/978-3-642-19318-7_31
Rights: "© 2011 Springer Berlin Heidelberg This is the author created version of a work that has been peer reviewed and accepted for publication by Saliency Density Maximization for Object Detection and Localizatio, Springer Berlin Heidelberg. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: http://dx.doi.org/10.1007/978-3-642-19318-7_31 ."
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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