Image quality assessment for visual object segmentation
An image segmentation model is deemed successful if reflects how a human would segment an image into foreground and background (i.e. be as close to the ground truth (GT) as possible). To train a successful model, the evaluation of binary foreground maps (FM) plays an important role. Recent evaluatio...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175092 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | An image segmentation model is deemed successful if reflects how a human would segment an image into foreground and background (i.e. be as close to the ground truth (GT) as possible). To train a successful model, the evaluation of binary foreground maps (FM) plays an important role. Recent evaluation methods like E-measure fail to consider the semantic meanings of images for binary FM evaluation. In this paper, I investigate the feasibility to perform binary FM evaluation using an attention map (Attention Measure), as
well as its effectiveness when integrated into E-measure, to make up for E-measure’s lack of semantic consideration in the form of a new measure, Aligned Attention Measure. Attention Measure showed promising results by itself, while Aligned Attention Measure achieved improvements to E-measure and managed to reflect a better human visual system (HVS) correlation in terms of ranking binary FMs. Future work will explore the fine-tuning of this metric to minimise variability as well as improve its parity with HVS. |
|---|