Deep Salience: Visual Salience Modeling via Deep Belief Propagation


  • Richard Jiang The University of Sheffield
  • Danny Crookes Queen’s University Belfast



Visual Salience, Markove Random Field, Hierachical Image Analysis, Unsupervised Object Detection


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 many state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the better scores in F-measure and mean-square error tests.




How to Cite

Jiang, R., & Crookes, D. (2014). Deep Salience: Visual Salience Modeling via Deep Belief Propagation. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).