Style classification and visualization of art painting’s genre using self-organizing maps

Lee, Sang-Geol; Cha, Eui-Young

doi:10.1186/s13673-016-0063-4

Cited by 20 publications

(10 citation statements)

References 11 publications

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“…Similarly, an auspicious pattern image also shows the frequency of tonal change. Several studies in the literature have discussed the applications of texture feature analysis to painting restoration [9][10][11], painting classification by artistic genre [12][13][14], painting style recognition [15,16], and raw material investigation [11,17]. Cai and Siegel [9] explored two texture features-energy (or homogeneity) and entropy (or disorderliness)-to model the visual appearance of paintings before and after surface varnishing.…”

Section: Texture Feature Analysis For Paintingsmentioning

confidence: 99%

Style Recognition of Door God Paintings by Hypothesis Testing for Texture Features of Painting Patterns

Wun

et al. 2022

Applied Sciences

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Many studies in the literature have presented multiple remote sensing techniques for defect inspection of paintings. At present, however, papers on defect inspection and restoration of oriental architectural arts—such as door god paintings—are still rare. If an aged and damaged door god painting needs a restoration, then following the style and treatment skill of the original artist as much as possible is important for the restoration. Unfortunately, it is usually difficult to access the original artists for some of the aged door god paintings. This paper considers the texture features of auspicious patterns of armors on warrior door gods as useful information to recognize styles of door god paintings by unknown artists. First, a two-level two-dimensional discrete wavelet transform coupled with co-occurrence matrix calculation was adopted to analyze the texture features, based on the descriptors of angular second moment (ASM), entropy (ENT), contrast (CON), homogeneity (HOM), dissimilarity (DIS), correlation (COR), and cluster tendency (CLU), in the four orientations of 0° (horizontal), 45° (vertical), and 90° and 135° (double diagonal). Second, a two-tailed t-test based on the analyzed texture features was introduced into the hypothesis testing for demonstrating the master and apprentice relationships between the surveyed artists, and for recognizing the door god painting styles of unknown artists as well. The experimental results show that the proposed method effectively describes the texture features of the auspicious patterns of the surveyed door god paintings, and is able to determine the useful co-occurrence features for recognizing unknown artists’ painting styles.

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Section: Texture Feature Analysis For Paintingsmentioning

confidence: 99%

Style Recognition of Door God Paintings by Hypothesis Testing for Texture Features of Painting Patterns

Wun

et al. 2022

Applied Sciences

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“…In some studies, self-organizing maps are used for art genre classification. For example, [11] proposed a methodology that classifis painting styles by extracting some features from the paintings. They achieved their goal by using self-organizing maps.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Derived Features on Art Genre Classification with Machine Learning

Abidin

2021

Sakarya University Journal of Science

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Classification of the artwork according to their genres is being done for years. Although this process was used to be done by art experts before, now artificial intelligence techniques may help people manage this classification task. The algorithms used for classification are already improved, and now they can make classifications and predictions for any kind of genre classification. In this study, two different machine learning algorithms are used on an artwork dataset for genre classification. The primary purpose of this study is to show that the derived features about the artwork have a remarkable effect on correct genre classification. These features are derived from the metadata of the dataset. This metadata contains information about the nationalities and the period that the artist lived. Image filters are also applied to the images but the results show that applying only image filters on the dataset used in the study did not perform well. Instead, adding derived features extracted from the metadata increased the classification performances dramatically.

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“…Color feature is important as the global features of an image [7]. We extract the color features through statistical computation of the overall pixel of the cropped images.…”

Section: Svm Classificationmentioning

confidence: 99%

An Ultra Lightweight CNN for Low Resource Circuit Component Recognition

Ju¹,

Chen²

2020

Preprint

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In this paper, we present an ultra lightweight system that can effectively recognize different circuit components in an image with very limited training data. Along with the system, we also release the data set we created for the task. A two-stage approach is employed by our system. Selective search was applied to find the location of each circuit component. Based on its result, we crop the original image into smaller pieces. The pieces are then fed to the Convolutional Neural Network (CNN) for classification to identify each circuit component. It is of engineering significance and works well in circuit component recognition in a low resource setting. The accuracy of our system reaches 93.4%, outperforming the support vector machine (SVM) baseline (75.00%) and the existing state-of-the-art RetinaNet solutions (92.80%).

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Style classification and visualization of art painting’s genre using self-organizing maps

Cited by 20 publications

References 11 publications

Style Recognition of Door God Paintings by Hypothesis Testing for Texture Features of Painting Patterns

Style Recognition of Door God Paintings by Hypothesis Testing for Texture Features of Painting Patterns

The Effect of Derived Features on Art Genre Classification with Machine Learning

An Ultra Lightweight CNN for Low Resource Circuit Component Recognition

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