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2017 | OriginalPaper | Chapter

Saliency Detection via CNN Coarse Learning and Compactness Based ELM Refinement

Authors : Ruirui Li, Shihao Sun, Lei Yang, Wei Hu

Published in: Computer Vision

Publisher: Springer Singapore

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Abstract

Salient object detection has attracted a lot of research in computer vision. It plays a vital role in image retrieval, object recognition and other image processing tasks. Although varieties of methods have been proposed, most of them heavily depend on feature selection and fail in the case of complex scenes. We propose a processing framework for saliency detection which contains two main steps. It uses deep convolutional neural networks (CNNs) to find a coarse saliency region map that includes semantic clues. Then it refines the coarse saliency map by training an extreme learning machine (ELM) on a group of color and texture compactness features. To get final saliency objects, it synthesizes the coarse saliency region map and several multiscale saliency maps that are obtained by refining the coarse one together. The method achieves good experimental results and can be used to improve the existing salient object detection methods as well.

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Metadata
Title
Saliency Detection via CNN Coarse Learning and Compactness Based ELM Refinement
Authors
Ruirui Li
Shihao Sun
Lei Yang
Wei Hu
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7302-1_37

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