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Evaluation on fusion of saliency and objectness for salient object segmentation

Published: 19 August 2015 Publication History

Abstract

Saliency detection measures the probability how a region attracts human visual attention, and objectness estimates the probability that a rectangle window may contain potential objects. Can a salient object segmentation method which utilizes both saliency and objectness achieve a better segmentation performance? To address this problem, this paper evaluates different fusion schemes to integrate saliency with objectness for effective salient object segmentation. Based on the saliency map generated by any saliency model and the pixel-level objectness map, the resultant fusion map is exploited to initialize salient object and background. Then a fusion map based salient object segmentation method under the framework of graph cut is proposed to obtain the final salient object segmentation result. We performed extensive experiments on two public datasets, and conclude that fusion of saliency and objectness generally facilitates to improve salient object segmentation performance compared to only using saliency or objectness, and the proposed segmentation method using a number of fusion schemes with saliency models outperforms the state-of-the-art salient object segmentation method.

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  • (2020)Data-Driven Skin Detection in Cluttered Search and Rescue EnvironmentsIEEE Sensors Journal10.1109/JSEN.2019.295978720:7(3697-3708)Online publication date: 1-Apr-2020
  • (2018)Spatiotemporal salient object detection by integrating with objectnessMultimedia Tools and Applications10.5555/3269690.326978277:15(19481-19498)Online publication date: 1-Aug-2018
  • (2017)Salient Object Segmentation via Effective Integration of Saliency and ObjectnessIEEE Transactions on Multimedia10.1109/TMM.2017.269302219:8(1742-1756)Online publication date: Aug-2017
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    cover image ACM Other conferences
    ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service
    August 2015
    397 pages
    ISBN:9781450335287
    DOI:10.1145/2808492
    • General Chairs:
    • Ramesh Jain,
    • Shuqiang Jiang,
    • Program Chairs:
    • John Smith,
    • Jitao Sang,
    • Guohui Li
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 19 August 2015

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    Author Tags

    1. fusion
    2. objectness map
    3. saliency map
    4. salient object segmentation

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    ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;
    Overall Acceptance Rate 163 of 456 submissions, 36%

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    Cited By

    View all
    • (2020)Data-Driven Skin Detection in Cluttered Search and Rescue EnvironmentsIEEE Sensors Journal10.1109/JSEN.2019.295978720:7(3697-3708)Online publication date: 1-Apr-2020
    • (2018)Spatiotemporal salient object detection by integrating with objectnessMultimedia Tools and Applications10.5555/3269690.326978277:15(19481-19498)Online publication date: 1-Aug-2018
    • (2017)Salient Object Segmentation via Effective Integration of Saliency and ObjectnessIEEE Transactions on Multimedia10.1109/TMM.2017.269302219:8(1742-1756)Online publication date: Aug-2017
    • (2017)Spatiotemporal salient object detection by integrating with objectnessMultimedia Tools and Applications10.1007/s11042-017-5334-177:15(19481-19498)Online publication date: 16-Nov-2017

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