Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

Deng-Ping Fan, Ming-Ming Cheng, Jiang-Jiang Liu, Shang-Hua Gao, Qibin Hou, Ali Borji; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 186-202

Abstract


We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-of-the-art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we rst identify 7 crucial aspects that a comprehensive and balanced dataset should fulll. Then, we propose a new high-quality dataset and update the previous saliency benchmark. Specically, our SOC (Salient Objects in Clutter) dataset, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes. Finally, we report attribute-based performance assessment on our dataset.

Related Material


[pdf]
[bibtex]
@InProceedings{Fan_2018_ECCV,
author = {Fan, Deng-Ping and Cheng, Ming-Ming and Liu, Jiang-Jiang and Gao, Shang-Hua and Hou, Qibin and Borji, Ali},
title = {Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}