Deep salience:Visual salience modeling via deep belief propagation

Jiang, Richard and Crookes, Danny (2014) Deep salience:Visual salience modeling via deep belief propagation. In: Proceedings of the National Conference on Artificial Intelligence. Proceedings of the National Conference on Artificial Intelligence . AI Access Foundation, CAN, pp. 2773-2779. ISBN 9781577356806

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Abstract

Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem's biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform eleven state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the best F-measure and mean-square error in the experiments.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 134370
Deposited By: ep_importer_pure
Deposited On: 22 Jun 2019 01:03
Refereed?: Yes
Published?: Published
Last Modified: 11 Feb 2020 06:40
URI: https://eprints.lancs.ac.uk/id/eprint/134370

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