NDLI: An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations

Content Provider	IEEE Xplore Digital Library
Author	Junwei Han Sheng He Xiaoliang Qian Dongyang Wang Lei Guo Tianming Liu
Copyright Year	1991
Abstract	Saliency detection aims at quantitatively predicting attended locations in an image. It may mimic the selection mechanism of the human vision system, which processes a small subset of a massive amount of visual input while the redundant information is ignored. Motivated by the biological evidence that the receptive fields of simple cells in V1 of the vision system are similar to sparse codes learned from natural images, this paper proposes a novel framework for saliency detection by using image sparse coding representations as features. Unlike many previous approaches dedicated to examining the local or global contrast of each individual location, this paper develops a probabilistic computational algorithm by integrating objectness likelihood with appearance rarity. In the proposed framework, image sparse coding representations are yielded through learning on a large amount of eye-fixation patches from an eye-tracking dataset. The objectness likelihood is measured by three generic cues called compactness, continuity, and center bias. The appearance rarity is inferred by using a Gaussian mixture model. The proposed paper can serve as a basis for many techniques such as image/video segmentation, retrieval, retargeting, and compression. Extensive evaluations on benchmark databases and comparisons with a number of up-to-date algorithms demonstrate its effectiveness.
Sponsorship	IEEE Circuits and Systems Society
Starting Page	2009
Ending Page	2021
Page Count	13
File Size	1491196
File Format	PDF
ISSN	10518215
Volume Number	23
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vision systems Image coding Computer vision Independent component analysis Probabilistic logic Feature extraction Gaussian mixture model visual attention Gaussian mixture models independent component analysis saliency sparse coding
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Media Technology

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