A fuzzy measure for objective evaluation of interactive image segmentation algorithms

Askari, Elham; Moghadam, Amir Masoud Eftekhari

doi:10.1109/icsipa.2011.6144102

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…One of the algorithms proposed for interactive segmentation [13] measures efficiency based on boundary accuracy and object accuracy. This evaluation scheme was presented in [14] and uses a fuzzy logic approach in addition to requiring ground truth data. For our algorithm, we are using a similar approach to [14].…”

Section: Objective Evaluationmentioning

confidence: 99%

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Robust image segmentation based on convex active contours and the Chan Vese model

Amin

2014

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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In this paper, we present a robust image segmentation technique based on the Geodesic Convex Active Contour (GCAC) and the Chan-Vese (CV) model. The proposed algorithm overcomes the drawbacks of existing interactive image segmentation techniques which are heavily dependent upon the initial user input. Here, we propose to start with a Geodesic based contour before using the Chan-Vese model. Contrary to the basic Geodesic model and the Random Walk technique, our algorithm works with minimal input and is shown to be independent of the location of the input pixels provided by the user. The algorithm works by initiating a contour based on the Geodesic distance which is then used with the Chan-Vese model to further refine the segmentation results. The combination of region-based and boundary-based segmentation techniques ensures that the proposed algorithm works well with all types of images. We tested the proposed algorithm on several standard databases using both subjective and objective measures. Our experimental results show that the proposed algorithm outperforms existing approaches over indoor and outdoor images in terms of both processing time and segmentation accuracy.

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Section: Objective Evaluationmentioning

confidence: 99%

“…This evaluation scheme was presented in [14] and uses a fuzzy logic approach in addition to requiring ground truth data. For our algorithm, we are using a similar approach to [14]. The metric is estimated on a region-by-region basis and then averaged over all the regions.…”

Section: Objective Evaluationmentioning

confidence: 99%

Robust image segmentation based on convex active contours and the Chan Vese model

Amin

2014

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Color image segmentation by combining the convex active contour and the Chan Vese model

Deriche

Amin

Qureshi

2017

Pattern Anal Applic

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A Method of Interactively Extracting Region Objects from High-Resolution Remote Sensing Image Based on Full Connection CRF

Zhang

2018

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)

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A fuzzy measure for objective evaluation of interactive image segmentation algorithms

Cited by 3 publications

References 18 publications

Robust image segmentation based on convex active contours and the Chan Vese model

Robust image segmentation based on convex active contours and the Chan Vese model

Color image segmentation by combining the convex active contour and the Chan Vese model

A Method of Interactively Extracting Region Objects from High-Resolution Remote Sensing Image Based on Full Connection CRF

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