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Modeling a Local Dissimilarity Map With Weibull Distribution-Application to 2-Class and Multi-Class Image Classification
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- Diaw, Moustapha (author)
- CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France
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- Delahaies, Agnes (author)
- CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France
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- Landré, Jérôme (author)
- CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France
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- (creator_code:org_t)
- IEEE, 2022
- 2022
- English.
- In: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 35750-35767
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Due to the considerable increase of images in everyday life, many applications require a study on their similarity. The main challenge is to find a simple and efficient method to compare and classify image pairs into similar and dissimilar classes. This study presents a new method to image pairs comparison and classification based on the modeling of the Local Dissimilarity Map (LDM). The LDM is a tool for locally measuring the dissimilarity between two binary or grayscale images. It is a measure of dissimilarities based on a modified version of the Hausdorff distance, which allows quantifying locally the dissimilarities between images. This measure is completely without parameters and generic. The image pairs classification (2-class classification) method is structured as follows. First, a statistical model for the LDM is proposed. The model parameters, used as descriptors, are relevant to discriminate similar and dissimilar image pairs. Second, classifiers are applied to compute the classification scores (2-class classification problem). In addition, this approach is robust with respect to geometric transformations such as translation compared to the state-of-the-art similarity measures. Although the main objective of this paper is to apply our approach to image pairs classification, it is also performed on a classification with more than two classes (multi-class classification). Experiments on the well-known image data sets ∗NIST and on old print data set prove that the proposed method produces comparable, even better results than the state-of-the-art methods in terms of accuracy and F(1) score.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Euclidean distance transform
- Local dissimilarity map
- supervised classification
- Weibull distribution
- Classification (of information)
- Classifiers
- Image classification
- Computational modelling
- Dissimilarity maps
- Euclidean distance
- Euclidean distance transforms
- Features extraction
- Gray scale
- Image pairs
- Index
- Mathematical transformations
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Jönköping University
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