Multi-scale salient object detection with pyramid spatial pooling

Date

2018

Authors

Zhang, Jing
Dai, Yuchao
Porikli, Fatih
He, Mingyi

Journal Title

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Volume Title

Publisher

IEEE

Abstract

Salient object detection is a challenging task in complex compositions depicting multiple objects of different scales. Albeit the recent progress thanks to the convolutional neural networks, the state-of-the-art salient object detection methods still fall short to handle such real-life scenarios. In this paper, we propose a new method called MP-SOD that exploits both Multi-Scale feature fusion and Pyramid spatial pooling to detect salient object regions in varying sizes. Our framework consists of a front-end network and two multi-scale fusion modules. The front-end network learns an end-to-end mapping from the input image to a saliency map, where a pyramid spatial pooling is incorporated to aggregate rich context information from different spatial receptive fields. The multi-scale fusion module integrates saliency cues across different layers, that is from low-level detail patterns to high-level semantic information by concatenating feature maps, to segment out salient objects with multiple scales. Extensive experimental results on eight benchmark datasets demonstrate the superior performance of our method compared with existing methods.

Description

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Citation

Source

Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017

Type

Conference paper

Book Title

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Access Statement

License Rights

DOI

10.1109/APSIPA.2017.8282222

Restricted until

2099-12-31