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.
Original language | English |
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Title of host publication | Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence |
Publisher | The AAAI Press |
Pages | 2773-2779 |
Number of pages | 7 |
ISBN (Print) | 978-1-57735-661-5 |
Publication status | Published - Jul 2014 |
Event | The Twenty-Eighth AAAI Conference on Artificial Intelligence - Quebec City, Canada Duration: 27 Jul 2014 → 31 Jul 2014 |
Conference
Conference | The Twenty-Eighth AAAI Conference on Artificial Intelligence |
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Country/Territory | Canada |
City | Quebec City |
Period | 27/07/2014 → 31/07/2014 |