A Coloring Algorithm for Image Classification

Gómez, Daniel; Montero, Javier; Poidomani, C.

doi:10.1142/9789812702661_0049

Cited by 13 publications

(27 citation statements)

References 9 publications

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“…As pointed out in GÓ and GÓ MEZ et al 2006(see also MONTERO, 2004GÓ MEZ et al, 2008a), coloring algorithms should play a key role as a decision-making aid under fuzziness, in the same way they already play a key role within crisp approaches. Understanding a problem quite often requires a nice picture that reduces information but keeps the relevant structure of the problem.…”

Section: Segmentation Of the Imagementioning

confidence: 98%

“…In this paper, we propose the use of these techniques to determine the training sites, necessary in any supervised classification. The segmentation algorithm that we propose here was developed in GÓ and modified for the use of fuzzy information in GÓ MEZ et al 2006. This algorithm is based on the well-known coloring problem for graphs.…”

Section: Segmentation Of the Imagementioning

confidence: 99%

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Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

Gómez

Montero

Biging

2008

Pure appl. geophys.

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This paper puts together some techniques that have been previously developed by the authors, but separately, relative to fuzzy classification within a remote sensing setting. Considering that each image can be represented as a graph that defines proximity between pixels, certain distances between the characteristic of contiguous pixels are defined on such a graph, so a segmentation of the image into homogeneous regions can be produced by means of a particular algorithm. Such a segmentation can be then introduced as information, previously to any classification procedure, with an expected significative improvement. In particular, we consider specific measures in order to quantify such an improvement. This approach is being illustrated with its application into a particular land surface problem.

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Section: Segmentation Of the Imagementioning

confidence: 98%

Section: Segmentation Of the Imagementioning

confidence: 99%

Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

Gómez

Montero

Biging

2008

Pure appl. geophys.

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“…Another advantage of hierarchical segmentation is that it can be used to build fuzzy boundaries by a hierarchical segmentation algorithm designed by the authors 150,152,153,154,155,156 .…”

Section: Hierarchical Image Segmentationmentioning

confidence: 99%

Classifying image analysis techniques from their output

Guada¹,

Gómez²,

Rodríguez³

et al. 2016

IJCIS

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In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

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“…This algorithm can be classified as an unsupervised, graph-based image segmentation technique (see [1,2,[23][24][25] for more details). The hierarchical approach defined here shares key elements of the segmentation algorithm proposed in [21] and extended in [20].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy image segmentation based upon hierarchical clustering

Gómez

Yáñez

Guada

et al. 2015

Knowledge-Based Systems

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In this paper we introduce the concept of Fuzzy Image Segmentation, providing an algorithm to build fuzzy boundaries based on the existing relations between the fuzzy boundary set problem and the (crisp) hierarchical image segmentation problem. In particular, since a crisp image segmentation can be characterized in terms of the set of edges that separates the adjacent regions of the segmentation, from these edges we introduce the concept of fuzzy image segmentation. Hence, each fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which can be then understood as the fuzzy boundary of the image. Some computational experiences are included in order to show the obtained fuzzy boundaries of some digital images.

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A Coloring Algorithm for Image Classification

Cited by 13 publications

References 9 publications

Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

Classifying image analysis techniques from their output

Fuzzy image segmentation based upon hierarchical clustering

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