Case-Based Reasoning for Image Segmentation by Watershed Transformation

Frucci, Maria; Perner, Petra; Baja, Gabriella Sanniti di

doi:10.1007/978-3-540-73180-1_11

Cited by 13 publications

(10 citation statements)

References 108 publications

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“…4. This architecture has been applied to threshold-based image segmentation [7] and the Watershed Transformation [17]. The resulting good segmentation quality compared to standard Watershed segmentation result is shown in Fig.…”

Section: Meta-learning For Parameter Selection Of Data/signal Processmentioning

confidence: 97%

“…6) and many of the ideas mentioned in the chapters before apply here. CBR-based meta-learning algorithms for parameter selection are preferable for the image pre-processing and segmentation unit [7] [17]. The mapping of the low-level features to the high-level features is a classification task for which a CBR-based algorithm can be applied.…”

Section: Case-based Image Interpretationmentioning

confidence: 99%

“…Among them are signal interpretation tasks, medical applications, and emerging applications such as geographic information systems, applications in biotechnology and topics in climate research (CBR commentaries) [17].…”

Section: Applicationsmentioning

confidence: 99%

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Mining Sparse and Big Data by Case-based Reasoning

Perner¹

2014

Procedia Computer Science

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The increasing use of digital media in daily life has resulted in a need for novel multimedia data analysis techniques. Case-based Reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, case-based reasoning can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. Therefore, CBR can mine sparse and big data. It can be seen as a learning and knowledge-discovery approach, since it can capture from new experience some general knowledge, such as case classes, prototypes and some higher-level concept. In this talk, we will explain the case-based reasoning process scheme. We will show what kinds of methods are necessary to provide all the functions for such a computer model. We will develop the bridge between CBR and Statistics and show how casebased reasoning can mine big and sparse data. Examples are being given based on multimedia applications. Finally, we will show recent new developments and we will give an outline for further work.

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Section: Meta-learning For Parameter Selection Of Data/signal Processmentioning

confidence: 97%

Section: Case-based Image Interpretationmentioning

confidence: 99%

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Mining Sparse and Big Data by Case-based Reasoning

Perner¹

2014

Procedia Computer Science

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“…This method is based on several existing image analysis methods, which are also present in the probability map method (Mičušík and Hanbury, 2006). A novel image segmentation scheme based on case-based reasoning was proposed to improve the performance of the watershed segmentation method (Frucci et al, 2008). It selects particular cases from a case base, which have image characteristics similar to that of the current input image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

Palenichka

Lakhssassi

Zaremba

2011

Engineering Applications of Artificial Intelligence

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“…Then, a number of papers suggesting solutions to the main problems affecting the watershed segmentation, or dealing with different applications of watershed transformation have been published (see, e.g., [11][12][13][14][15][16]). …”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Image Segmentation via Watershed Transformation

Frucci

Baja

2011

Image Analysis and Processing – ICIAP 2011

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Abstract.A new segmentation method is presented. The watershed transformation is initially computed starting from all seeds detected as regional minima in the gradient image and a digging cost is associated to each pair of adjacent regions. Digging is performed for each pair of adjacent regions for which the cost is under a threshold, whose value is computed automatically, so originating a reduced set of seeds. Watershed transformation and digging are repeatedly applied, until no more seeds are filtered out. Then, region merging is accomplished, based on the size of adjacent regions.

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Case-Based Reasoning for Image Segmentation by Watershed Transformation

Cited by 13 publications

References 108 publications

Mining Sparse and Big Data by Case-based Reasoning

Mining Sparse and Big Data by Case-based Reasoning

Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

A New Algorithm for Image Segmentation via Watershed Transformation

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