Unconstrained salient object detection via proposal subset optimization
Files
Published version
Date
2016
Authors
Zhang, Jianming
Sclaroff, Stan
Lin, Zhe
Shen, Xiaohui
Price, Brian
Mech, Radomir
Version
Published version
OA Version
Citation
J Zhang, S Sclaroff, Z Lin, X Shen, B Price, R Mech. 2016. "Unconstrained salient object detection via proposal subset optimization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Abstract
We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that directly outputs a compact set of detection windows, if any, for an input image. Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects. Location proposals tend to be highly overlapping and noisy. Based on the Maximum a Posteriori principle, we propose a novel subset optimization framework to generate a compact set of detection windows out of noisy proposals. In experiments, we show that our subset optimization formulation greatly enhances the performance of our system, and our system attains 16-34% relative improvement in Average Precision compared with the state-of-the-art on three challenging salient object datasets.
Description
License
This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark on the paper, it is identical to the version available on IEEE Xplore.