Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67525
Title: Learning to detect saliency with deep structure
Authors: Hu, Y
Chen, Z
Chi, Z 
Fu, H
Issue Date: 2015
Source: 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, Hong Kong, China, 9-12 Oct 2015, p.1770-1775
Abstract: Deep learning has shown great successes in solving various problems of computer vision. To the best of our knowledge, however, little existing work applies deep learning to saliency modeling. In this paper, a new saliency model based on convolutional neural network is proposed. The proposed model is able to produce a saliency map directly from an image's pixels. In the model, multi-level output values are adopted to simulate continuous values in a saliency map. Differing from most neural networks that use a relatively small number of output nodes, the output layer of our model has a large number of nodes. To make the training more efficient, an improved learning algorithm is adopted to train the model. Experimental results show that the proposed model succeeds in generating acceptable saliency maps after proper training.
Keywords: Convolutional neural network
Saliency detection
Saliency map
Deep learning
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-4799-8697-2 (electronic)
978-1-4799-8696-5 (USB)
DOI: 10.1109/SMC.2015.310
Appears in Collections:Conference Paper

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