NDLI: Saliency detection based on feature learning using Deep Boltzmann Machines

Content Provider	IEEE Xplore Digital Library
Author	Shifeng Wen Junwei Han Dingwen Zhang Lei Guo
Copyright Year	2014
Description	Author affiliation: Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China (Shifeng Wen; Junwei Han; Dingwen Zhang; Lei Guo)
Abstract	Saliency detection has been a very active research area in recent years. Most traditional methods suffer from the problem that existing visual features are not discriminative or not robust enough to predict salient locations. As a result, the experimental results of these previous methods are still far from satisfactory. In this paper, we propose to utilize a two-layer Deep Boltzmann Machine (DBM) to learn enhanced features from existing contrast-based low-level features, which are more discriminative and reliable. A saliency computation model is then trained to build a mapping from those enhanced features to eye fixation data. The proposed work is amongst the earliest efforts of examining the feasibility of applying deep learning algorithms to saliency detection. Comprehensive evaluations on two publically available benchmark datasets and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness of the proposed work.
Starting Page	1
Ending Page	6
File Size	1398799
Page Count	6
File Format	PDF
ISBN	9781479947614
DOI	10.1109/ICME.2014.6890224
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Visualization Computational modeling Training Data models Image color analysis Educational institutions Deep Boltzmann Machine Saliency detection deep learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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