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International Journal of Computer Vision

Erschienen in:

01.11.2016

Detection of Co-salient Objects by Looking Deep and Wide

verfasst von: Dingwen Zhang, Junwei Han, Chao Li, Jingdong Wang, Xuelong Li

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2016

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Abstract

In this paper, we propose a unified co-salient object detection framework by introducing two novel insights: (1) looking deep to transfer higher-level representations by using the convolutional neural network with additional adaptive layers could better reflect the sematic properties of the co-salient objects; (2) looking wide to take advantage of the visually similar neighbors from other image groups could effectively suppress the influence of the common background regions. The wide and deep information are explored for the object proposal windows extracted in each image. The window-level co-saliency scores are calculated by integrating the intra-image contrast, the intra-group consistency, and the inter-group separability via a principled Bayesian formulation and are then converted to the superpixel-level co-saliency maps through a foreground region agreement strategy. Comprehensive experiments on two existing and one newly established datasets have demonstrated the consistent performance gain of the proposed approach.

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Titel: Detection of Co-salient Objects by Looking Deep and Wide
verfasst von: Dingwen Zhang
Junwei Han
Chao Li
Jingdong Wang
Xuelong Li
Publikationsdatum: 01.11.2016
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2016
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0907-4

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