Image Segmentation from Consensus Information

Cho, Kyu-Jin; Meer, Peter

doi:10.1006/cviu.1997.0546

Cited by 61 publications

(24 citation statements)

References 36 publications

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“…In pioneering works [10], images of a same scene obtained from various modalities were merged and optimized based on edge information. Later, statistical analysis of the co-occurrence probability of neighbour pixels was considered to improve the accuracy of a partition from several versions with slightly disturbed borders [11].…”

Section: Segmentation Fusionmentioning

confidence: 99%

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

Randrianasoa

Kurtz

Desjardin

et al. 2015

Lecture Notes in Computer Science

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Abstract. Image segmentation is generally performed in a "one image, one algorithm" paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant information. To solve the induced segmentation fusion issues, various strategies have been already investigated for allowing a consensus between several segmentation outputs. This article proposes a contribution to segmentation fusion, with a specific focus on the "n images" part of the paradigm. Its main originality is to act on the segmentation research space, i.e., to work at an earlier stage than standard segmentation fusion approaches. To this end, an algorithmic framework is developed to build a binary partition tree in a collaborative fashion, from several images, thus allowing to obtain a unified hierarchical segmentation space. This framework is, in particular, designed to embed consensus policies inherited from the machine learning domain. Application examples proposed in remote sensing emphasise the potential usefulness of our approach for satellite image processing.

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Section: Segmentation Fusionmentioning

confidence: 99%

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

Randrianasoa

Kurtz

Desjardin

et al. 2015

Lecture Notes in Computer Science

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“…Merging approaches recursively merge similar regions (e.g. [14], [1]). "Divide & Conquer" approaches recursively split regions into distinct sub-regions (e.g.…”

Section: Brief Review Of Segmentation Approachesmentioning

confidence: 99%

Class-Specific, Top-Down Segmentation

Borenstein

Ullman

2002

Lecture Notes in Computer Science

290

285

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Abstract. In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined.

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“…Cho and Meer [5] propose a new approach for unsupervised segmentation based on RAG. This approach is derived from the consensus of a set of different segmentation outputs on one input image.…”

Section: Consensus Image Segmentationmentioning

confidence: 99%

“…Pyramid segmentation algorithms exhibit interesting properties with respect to segmentation algorithms based on a single representation. Thus, local operations can adapt the pyramidal hierarchy to the topology of the image, allowing the detection of global features of interest and representing them at low resolution levels [4][5][6]. Bister et al [4] emphasize the reduction of noise and the processing of local and global features within the same framework.…”

Section: Introductionmentioning

confidence: 99%

Pyramid segmentation algorithms revisited

Marfil

Molina-Tanco

Bandera

et al. 2006

Pattern Recognition

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The main goal of this work is to compare pyramidal structures proposed to solve segmentation tasks. Segmentation algorithms based on regular and irregular pyramids are described, together with the data structures and decimation procedures which encode and manage the information in the pyramid. In order to compare the different segmentation algorithms, we have employed three types of quality measurements: the shift variance measure, the F function and the Q function.

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Image Segmentation from Consensus Information

Cited by 61 publications

References 36 publications

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

Class-Specific, Top-Down Segmentation

Pyramid segmentation algorithms revisited

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