Salient object cutout using Google images

Hongyuan Zhu, Jianfei Cai, Jianmin Zheng, Jianxin Wu, Nadia Thalmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


Given any image input by users, how to automatically cutout the object-of-interest is a challenging problem due to lack of information of the object-of-interest and the background. Saliency detection techniques are able to provide some rough information about object-of-interest since they highlight high-contrast or high attention regions or pixels. However, the generated saliency map is often noisy and directly applying it for segmentation often leads to erroneous results. Motivated by the recent progress on image co-segmentation and internet image retrieval techniques, in this paper, we propose to use the user input image for segmentation as a query image to Google Images and then employ the top returned Google images to build up the knowledge about the object-of-interest in the user input image. Particularly, we develop a lightweight algorithm to learn the knowledge of the object-of-interest in the retrieved images to enhance the saliency map of the input image. Then, the enhanced saliency map is used to initialize the graph-cut to extract the object-of-interest. Experiments with the Mcgill dataset and multiple challenge cases demonstrate the effectiveness of our method in terms of producing a clean cutout.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781467357609
Publication statusPublished - 2013
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems 2013 - Beijing, China
Duration: 19 May 201323 May 2013 (Proceedings)


ConferenceIEEE International Symposium on Circuits and Systems 2013
Abbreviated titleISCAS 2013
Internet address

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