Analysis of Diagnostic Images of Artworks and Feature Extraction: Design of a Methodology

Amura, Annamaria; Aldini, Alessandro; Pagnotta, Stefano; Salerno, Emanuele; Tonazzini, Anna; Triolo, Paolo

doi:10.3390/jimaging7030053

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…With the continuous improvement of computer image processing technology, more and more people put forward higher demand for art works. Domestic and foreign experts have also deeply studied the field of image classification at home and abroad and achieved many remarkable results [ 5 ]. Hua et al selected Dunhua murals and Oriental murals for research, analyzed the artistic style attributes of painting works, formulated the similarity rules of artistic style, and classified the artistic style of works by calculating the similarity coefficient between images [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

Art Painting Image Classification Based on Neural Network

Liu

2022

Computational Intelligence and Neuroscience

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Neural network (NN) is among the most important and vital form of artificial intelligence which are utilized for the classification of data, information, or images. Moreover, NN has been extensively utilized in various research domains throughout the world, and it is because of overwhelming properties. Painting is a form formed by China’s long history and culture, and a large number of paintings reflect the living conditions of China in different periods, which is of great value to the development of China’s culture. Image classification has become a key research content in the field of image in the stage of rapid development of information technology, and the content of art painting image classification has also developed rapidly. At present, most traditional image classification methods are formed on the basis of shallow structure learning algorithm, and there are many types of image features that can be extracted, but some features will be lost when extracting, and we need to master the basic painting knowledge. As a result, this extraction process is not general, which explains why traditional Chinese art picture classification is not ubiquitous. The fast development of big data technology and neural network algorithms in recent years has the potential to speed up the categorization of art painting images. As a result, this research investigates the use of neural networks to classify art painting images. The painting image classification method based on artistic style is used to determine the styles of distinct creative works, and the painting image classification algorithm based on saliency is then used to categorize the picture semantics. Finally, a dataset for testing the categorization impact of art painting pictures is developed. The results show that the neural network algorithm can significantly improve the classification effect of art painting images with higher accuracy.

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Section: Related Workmentioning

confidence: 99%

Art Painting Image Classification Based on Neural Network

Liu

2022

Computational Intelligence and Neuroscience

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“…Vector data is what most people think of when they think about spatial data [1] [2]. Data in this format consists of points, lines, or polygons [3]. At the simplest level, vector data consists of individual points stored as coordinate pairs that represent physical locations in the world [4][5] [6].…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Vector and Raster Data for The Calculation of Landscape Environment Using a Geographic Information System Approach

Giofandi¹,

Munibah²,

Kraugusteeliana

et al. 2023

ITJRD

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GIS-based measurements can combine vector and raster data to produce thematic data obtained from remote sensing data. The data used in this study uses data on land use in the city of Pekanbaru. After the data is obtained, the pixel calculation process is carried out using three methods: the cell center method, the maximum area, and the maximum combined area. This data describes information with multiple raster data resolutions and then interprets the level of distortion in the data. The research findings found that in the process of raster data from 8 different resolution levels for the 5x5 meter category, it is able to provide results that are closest to the area of vector data, where PL 1 code produces 404229 pixels, PL 2 code ranges from 225717 pixels, PL 3 code ranges from 160323 pixels, code PL 4 ranges from 92268 pixels, PL 5 code reaches 73384 pixels, PL 6 code reaches 57237 pixels, and PL 7 code reaches 48315 pixels. Meanwhile, of the 3 methods that were compared to determine distortion with vector data, the cell center approach was the closest to raster data by calculation through eight levels of raster resolution compared to the other two methods. In choosing the right pixel resolution for further use in mathematical modeling, it is necessary to pay attention to the level of resolution by generalizing the resolution of satellite imagery data so that the data can have the same resolution. The weakness of the three methods lies in increasing the resolution the greater it will make the data coarser. This research is expected to be used as a consideration in future research to add a more precise process and be able to produce less storage capacity.

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“…Obtaining the exact reproduction and explanation of an artwork was one of the first developments in the first area, which includes the process of archiving, retrieving and disseminating data and derives all the benefits from the digital format [1][2][3][4][5][6]. In the second area of imaging diagnostics, digital images are used to detect and document the state of preservation of artifacts [7], as in the case of the noninvasive techniques based on imaging in different spectral regions used for the investigation of paintings [8]. In the third area, the image processing techniques can be used as a guide to the actual restoration of fine arts (computer-guided restoration), or they can produce a digitally restored version of the artwork.…”

Section: Introductionmentioning

confidence: 99%

Classification of Geometric Forms in Mosaics Using Deep Neural Network

et al. 2021

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The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

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Analysis of Diagnostic Images of Artworks and Feature Extraction: Design of a Methodology

Cited by 8 publications

References 27 publications

Art Painting Image Classification Based on Neural Network

Art Painting Image Classification Based on Neural Network

The Comparison of Vector and Raster Data for The Calculation of Landscape Environment Using a Geographic Information System Approach

Classification of Geometric Forms in Mosaics Using Deep Neural Network

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