Visual Quality Evaluation of Image Object Segmentation: Subjective Assessment and Objective Measure

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 jud...

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Vydáno v:IEEE transactions on image processing Ročník 24; číslo 12; s. 5033 - 5045
Hlavní autoři: Ran Shi, King Ngi Ngan, Songnan Li, Paramesran, Raveendran, Hongliang Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Databases, Factual - classification
Databases, Factual - standards
Distortion measurement
Humans
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - standards
Image segmentation
Object segmentation
Objective Measure
Onions
Semantics
Subjective Evaluation
Visual databases
Visual Quality
Visualization
objective measure
visual quality
subjective evaluation
Object segmentation
ISSN:1057-7149, 1941-0042
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Shrnutí: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.
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2015.2473099