The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Enhanced-alignment Measure for Binary Foreground Map Evaluation
Open accessAbstract
The existing binary foreground map (FM) measures address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes. In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). Our measure combines local pixel values with the image-level mean value in one term, jointly capturing image-level statistics and local pixel matching information. We demonstrate the superiority of our measure over the available measures on 4 popular datasets via 5 meta-measures, including ranking models for applications, demoting generic, random Gaussian noise maps, ground-truth switch, as well as human judgments. We find large improvements in almost all the meta-measures. For instance, in terms of application ranking, we observe improvement ranging from 9.08% to 19.65% compared with other popular measures.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Cited by
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Self-citationUC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00861 · Cited by 423
Self-citationSalient Object Detection via Integrity Learning
Zhuge, M., Fan, D.-P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179526 · Cited by 403
Self-citationRGB-D salient object detection: A survey
Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
Self-citationEDN: Salient Object Detection via Extremely-Downsampled Network
Wu, Y.-H., Liu, Y., Zhang, L., Cheng, M.-M., & Ren, B. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3164550 · Cited by 282
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Metadata
- Authors
- Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., & Borji, A.
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 698-704
- OpenAlex
- W2963529609
- Cited by
- 1,607
- References
- 2
- FWCI
- 24.15
- Type
- conference paper
- Language
- en
- Open access
- bronze
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