Buffering Fuzzy Maps in GIS

Guesgen, Hans W.; Hertzberg, Joachim; Lobb, Richard J.; Mantler, Andrea

doi:10.1207/s15427633scc032&3_07

Cited by 1 publication

(1 citation statement)

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“…Let us consider the fuzzy matching for the mixing images on the input images [10]. .The degree to which the input target images satisfy the conditions of fuzzy rules and conditions .Suppose IMAGE X is defined by rules R 1 and IMAGES Y is defined by rules R 2 .In this case the matching degree will be represented by as follows:…”

Section: Fuzzy Matchingmentioning

confidence: 99%

Fuzzy Logic over Ontological Annotation and Classification for Spatial Feature Extraction

Kamal¹,

Nimmy²,

Chowdhury³

2012

IJCA

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With the advent of new web technology, Image Annotation and Classification has paved the way for invoking an efficient and effective research area as it is of immense importance in searching images from different categories of relevant images using keywords. This may be an impressive tool in describing image content as object or textual information to classify images. To serve this purpose, many techniques have been lunched for automatic image annotation and classification based on content and exit metadata. Automatic image annotation however, is highly difficult and challengeable. So users have to follow the annotation manually. In this paper, we applied fuzzy logic implication and fuzzy set operation for Historical image classification. We have compared the outcome of how fuzzy classification is better ontological image classification. Fuzzy logic plays an important rule so that the margin of the classification becomes more accurate. Here we imposed fuzzy matrix optimization for Spatial Image classification of Historical image data. Fuzzy matrix determines the optimal values of spatial data which are near about correct with less uncertainty. Fuzzy membership function also works to estimate the values before using in fuzzy matrix. We also pro-posed a manual method for image annotation based on IPTC metadata with a view to retrieving images with its corresponding information for automatic semantic ontological and fuzzy classification using linked data. We strived to experiment on about 400 images of different historical heritages.

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Section: Fuzzy Matchingmentioning

confidence: 99%