Image quality assessment for visual object segmentation - Ng, Darryl Jingheng (Nanyang Technological University, 2024)
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.