(An Ongoing Project…)
Image and Video Processing Lab, The
Object segmentation is a challenge and important task in the computer vision. In the past decade, a lager number of object segmentation methods have been proposed, meanwhile it still lacks the reliable way for evaluating the methods’ performance. In the traditional objective object segmentation metrics, they just use the area and distance information to measure the difference between segmentation result and reference (ground truth) without including any perceptual factors  as shown in Fig.1. In this project, we aim at designing an objective object segmentation metric which is derived on the basis of perceptual information through subjective experiments and can be matched with subjective evaluation.
Fig.1 from left to right is the original image, reference  and segmented object , respectively. The segmented object obtains a low subjective evaluation grade, but a high grade using traditional objective metric.
Fig.2 our subjective evaluation software
2. Group members
- Research Assistant: Ran SHI (firstname.lastname@example.org)
- Supervisor: King Ngi Ngan (email@example.com)
 Elisa Drelie Gelasca and Touradj Ebrahimi, “On Evaluating Video Object Segmentation Quality: A Perceptually Driven Objective Metric”, IEEE journal of selected topics in signal processing, vol. 3, no. 2, pp. 319-335.Apr 2009.
R. Achanta, S. Hemami, F.
 E. Rahtu, J. Kannala, M. Salo, and J. Heikkila, “Segmenting salient objects from images and videos”, Proc. ECCV, pp.366-379, Sept. 2010.