A supervised evaluation method based on region shape descriptor for image segmentation algorithm

Vojodi, Hakimeh; Moghadam, Amir Masoud Eftekhary

doi:10.1109/aisp.2012.6313710

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Jaccard index [11], Dice coefficient (a.k.a F-measure) [12,13], based on spatial overlapping information. From such indices, many frameworks for segmentation quality measures were developed [14,15,16,17,18,19], based from example on region or contour-based strategies.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the quality of binary partition trees based on uncertain semantic ground-truth for image segmentation

Randrianasoa

Kurtz

Gançarski

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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The binary partition tree (BPT) is a hierarchical data-structure that models the content of an image in a multiscale way. In particular, a cut of the BPT of an image provides a segmentation, as a partition of the image support. Actually, building a BPT allows for dramatically reducing the search space for segmentation purposes, based on intrinsic (image signal) and extrinsic (construction metric) information. A large literature has been devoted to the construction on such metrics, and the associated choice of criteria (spectral, spatial, geometric, etc.) for building relevant BPTs, in particular in the challenging context of remote sensing. But, surprisingly, there exists few works dedicated to evaluate the quality of BPTs, i.e. their ability to further provide a satisfactory segmentation. In this paper, we propose a framework for BPT quality evaluation, in a supervised paradigm. Indeed, we assume that ground-truth segments are provided by an expert, possibly with a semantic labelling and a given uncertainty. Then, we describe local evaluation metrics, BPT nodes / ground-truth segments fitting strategies, and global quality score computation considering semantic information, leading to a complete evaluation framework. This framework is illustrated in the context of BPT segmentation of multispectral satellite images.

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Section: Introductionmentioning

confidence: 99%

Evaluating the quality of binary partition trees based on uncertain semantic ground-truth for image segmentation

Randrianasoa

Kurtz

Gançarski

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

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Shape description losses for medical image segmentation

Fang

Xia

et al. 2023

Machine Vision and Applications

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A Survey of Object Co-Segmentation

Liu

2019

IEEE Access

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It is widely acknowledged that object segmentation is a significant research field for computer vision and a key process for many other visual tasks. In the past unsupervised single-image segmentation, there are often cases where the segmentation result is not good. In the current supervised single-image segmentation, it is necessary to rely on a large number of data annotations and long-term training of the model. Then, people attempted to segment simultaneously the common regions from multiple images. On the one hand, it does not need to use a large amount of labeled data to train in advance. On the other hand, it utilizes the consistency constraint between images to better obtain the object information. This idea can generate better performance than the traditional one did, resulting in many methods related to object cosegmentation. This paper reviews some classic and effective object co-segmentation methods, including saliency-based approaches, joint-processing-based approaches, graph-based approaches, and others. For different methods, we select two or three related models to elaborate, such as a model based on random walks. Moreover, in order to exhibit and evaluate these methods objectively and comprehensively, we not only summarize them in the form of flowcharts and algorithm summaries, but also compare their performance with visualization methods and evaluation metrics, such as intersection-over-union, consistency error, and precision-recall rate. From the experiment, we also attempt to clarify and analyze the existing problems. Finally, we point out the challenges and directions and open new venues for future researchers in the field.

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A supervised evaluation method based on region shape descriptor for image segmentation algorithm

Cited by 3 publications

References 9 publications

Evaluating the quality of binary partition trees based on uncertain semantic ground-truth for image segmentation

Evaluating the quality of binary partition trees based on uncertain semantic ground-truth for image segmentation

Shape description losses for medical image segmentation

A Survey of Object Co-Segmentation

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