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Saliency detection using hierarchical manifold learning

journal contribution
posted on 2015-11-30, 00:00 authored by Youhai Qiu, Xiangping Sun, Fenghua She
Saliency detection is critical to many applications in computer vision by eliminating redundant backgrounds. The saliency detection approaches can be divided into two categories, i.e., top-down and bottom-up. Among them, bottom-up models have attracted more attention due to their simple mechanisms. However, many existing bottom-up models are not robust to crowded backgrounds because of missing salient regions within feedforward frameworks which is often not effective for complex scenes. We tackle these problems by modifying and extending a bottom-up saliency detection model through three phases, (1) constructing a hierarchical sequence of images from the perspective of entropy, (2) estimated mid-level cues are used as feedback information, (3) subsequently generating saliency maps by global context and local uniqueness in a graph-based framework. We also compare the proposed bottom-up model with state-of-the-art approaches on two benchmark datasets to evaluate its saliency detection performance. The experimental results demonstrate that the proposed bottom-up saliency detection approach is not only robust to both cluttered and clean scenes, but also able to obtain objects with different scales.

History

Journal

Neurocomputing

Volume

168

Pagination

538 - 549

ISSN

0925-2312

eISSN

1872-8286

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2015, Elsevier

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