
pmid: 26316130
A visual quality evaluation of image object segmentation as one member of the visual quality evaluation family has been studied over the years. Researchers aim at developing the objective measures that can evaluate the visual quality of object segmentation results in agreement with human quality judgments. It is also significant to construct a platform for evaluating the performance of the objective measures in order to analyze their pros and cons. In this paper, first, we present a novel subjective object segmentation visual quality database, in which a total of 255 segmentation results were evaluated by more than thirty human subjects. Then, we propose a novel full-reference objective measure for an object segmentation visual quality evaluation, which involves four human visual properties. Finally, our measure is compared with some state-of-the-art objective measures on our database. The experiment demonstrates that the proposed measure performs better in matching subjective judgments. Moreover, the database is available publicly for other researchers in the field to evaluate their measures.
Databases, Factual, Image Processing, Computer-Assisted, Humans, Algorithms
Databases, Factual, Image Processing, Computer-Assisted, Humans, Algorithms
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 32 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
