Deep neural network for foreground object segmentation: An unsupervised approach

Majumder, A and Venkatesh Babu, R (2018) Deep neural network for foreground object segmentation: An unsupervised approach. In: 6th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2017, 16 - 19 December 2017, Mandi, pp. 360-371.

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Abstract

Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos have been a well established problem of computer vision. While segmenting salient objects from images depend only on static information, temporal information in a video can make non salient objects be salient due to movement. Besides the temporal information, there are other challenges involved with video segmentation, such as 3D parallax, camera shake, motion blur, etc. In this work, we propose a novel unsupervised end to end trainable, fully convolutional deep neural network for object segmentation. Our model is robust and scalable across scenes, as it is tested unsupervisedly and can easily infer which objects constitute the foreground of the image. We run various tests on two well established benchmarks of video object segmentation, DAVIS and FBMS-59 datasets. We report our results and compare them against the state of the art methods.

Item Type:	Conference Poster
Publication:	Communications in Computer and Information Science
Publisher:	Springer Verlag
Additional Information:	The copyright for this article belongs to the Springer Nature Singapore Pte Ltd.
Keywords:	Computer vision; Deep neural networks; Geometrical optics, Foreground segmentation; Image saliencies; Object segmentation; State-of-the-art methods; Temporal information; Unsupervised approaches; Video-object segmentation; Visual saliency, Image segmentation
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	26 Aug 2022 06:25
Last Modified:	26 Aug 2022 06:25
URI:	https://eprints.iisc.ac.in/id/eprint/76074

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