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Superpixel Level Incremental Visual Saliency Detection in Low Contrast Video

Published: 24 February 2017 Publication History

Abstract

The last few decades have witnessed the rapid development of saliency detection, which can automatically extract object-of-interest from clutter scene. However, visual saliency detection in low contrast video stream still remains a challenge. In this paper, we present a saliency detection model to detect salient object in low contrast video, which combines spatial saliency computation with temporal saliency computation. In spatial domain, superpixel segmentation and boundary prior are utilized to detect salient object in single video frame. And incremental learning algorithm is employed in temporal domain to effectively update the background model. Extensive experiments demonstrate that this method can achieve better performance.

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cover image ACM Other conferences
ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing
February 2017
545 pages
ISBN:9781450348171
DOI:10.1145/3055635
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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  • Southwest Jiaotong University

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Published: 24 February 2017

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Author Tags

  1. Low contrast video
  2. boundary prior
  3. incremental learning
  4. saliency detection
  5. superpixel segmentation

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