Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/188094
Title: Evaluation of Segmentation Quality via Adaptive Composition of Reference Segmentations
Authors: Bo Peng;Lei Zhang;Xuanqin Mou;Ming-Hsuan Yang
Year: 2017
Publisher: IEEE
Abstract: Evaluating image segmentation quality is a critical step for generating desirable segmented output and comparing performance of algorithms, among others. However, automatic evaluation of segmented results is inherently challenging since image segmentation is an ill-posed problem. This paper presents a framework to evaluate segmentation quality using multiple labeled segmentations which are considered as references. For a segmentation to be evaluated, we adaptively compose a reference segmentation using multiple labeled segmentations, which locally matches the input segments while preserving structural consistency. The quality of a given segmentation is then measured by its distance to the composed reference. A new dataset of 200 images, where each one has 6 to 15 labeled segmentations, is developed for performance evaluation of image segmentation. Furthermore, to quantitatively compare the proposed segmentation evaluation algorithm with the state-of-the-art methods, a benchmark segmentation evaluation dataset is proposed. Extensive experiments are carried out to validate the proposed segmentation evaluation framework.
URI: http://localhost/handle/Hannan/188094
volume: 39
issue: 10
More Information: 1929,
1941
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7723880.pdf1.34 MBAdobe PDF
Title: Evaluation of Segmentation Quality via Adaptive Composition of Reference Segmentations
Authors: Bo Peng;Lei Zhang;Xuanqin Mou;Ming-Hsuan Yang
Year: 2017
Publisher: IEEE
Abstract: Evaluating image segmentation quality is a critical step for generating desirable segmented output and comparing performance of algorithms, among others. However, automatic evaluation of segmented results is inherently challenging since image segmentation is an ill-posed problem. This paper presents a framework to evaluate segmentation quality using multiple labeled segmentations which are considered as references. For a segmentation to be evaluated, we adaptively compose a reference segmentation using multiple labeled segmentations, which locally matches the input segments while preserving structural consistency. The quality of a given segmentation is then measured by its distance to the composed reference. A new dataset of 200 images, where each one has 6 to 15 labeled segmentations, is developed for performance evaluation of image segmentation. Furthermore, to quantitatively compare the proposed segmentation evaluation algorithm with the state-of-the-art methods, a benchmark segmentation evaluation dataset is proposed. Extensive experiments are carried out to validate the proposed segmentation evaluation framework.
URI: http://localhost/handle/Hannan/188094
volume: 39
issue: 10
More Information: 1929,
1941
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7723880.pdf1.34 MBAdobe PDF
Title: Evaluation of Segmentation Quality via Adaptive Composition of Reference Segmentations
Authors: Bo Peng;Lei Zhang;Xuanqin Mou;Ming-Hsuan Yang
Year: 2017
Publisher: IEEE
Abstract: Evaluating image segmentation quality is a critical step for generating desirable segmented output and comparing performance of algorithms, among others. However, automatic evaluation of segmented results is inherently challenging since image segmentation is an ill-posed problem. This paper presents a framework to evaluate segmentation quality using multiple labeled segmentations which are considered as references. For a segmentation to be evaluated, we adaptively compose a reference segmentation using multiple labeled segmentations, which locally matches the input segments while preserving structural consistency. The quality of a given segmentation is then measured by its distance to the composed reference. A new dataset of 200 images, where each one has 6 to 15 labeled segmentations, is developed for performance evaluation of image segmentation. Furthermore, to quantitatively compare the proposed segmentation evaluation algorithm with the state-of-the-art methods, a benchmark segmentation evaluation dataset is proposed. Extensive experiments are carried out to validate the proposed segmentation evaluation framework.
URI: http://localhost/handle/Hannan/188094
volume: 39
issue: 10
More Information: 1929,
1941
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7723880.pdf1.34 MBAdobe PDF