Multi-View Feature Combination for Ancient Paintings Chronological Classification

Chen, Long; Chen, Jianda; Zou, Qin; Huang, Kai; Li, Qingquan

doi:10.1145/3003435

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…The latter works are essential to developing the segmentation and matching methods proposed within the research program of the project "GRAVITATE" [10]. Following the same registration workflow, but in a 2D-space, the broadly used SIFT [11] descriptor is calculated to register various types of heritage artifacts, such as fresco fragments [12], ancient paintings [13], and historical buildings [14]. However, feature-based approaches require sufficient overlap to estimate reliable transformations for data alignment.…”

Section: Related Workmentioning

confidence: 99%

A Generative and Entropy-Based Registration Approach for the Reassembly of Ancient Inscriptions

Lima-Hernandez

Vergauwen

2021

Remote Sensing

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An increased interest in computer-aided heritage reconstruction has emerged in recent years due to the maturity of sophisticated computer vision techniques. Concretely, feature-based matching methods have been conducted to reassemble heritage assets, yielding plausible results for data that contains enough salient points for matching. However, they fail to register ancient artifacts that have been badly deteriorated over the years. In particular, for monochromatic incomplete data, such as 3D sunk relief eroded decorations, damaged drawings, and ancient inscriptions. The main issue lies in the lack of regions of interest and poor quality of the data, which prevent feature-based algorithms from estimating distinctive descriptors. This paper addresses the reassembly of damaged decorations by deploying a Generative Adversarial Network (GAN) to predict the continuing decoration traces of broken heritage fragments. By extending the texture information of broken counterpart fragments, it is demonstrated that registration methods are now able to find mutual characteristics that allow for accurate optimal rigid transformation estimation for fragments alignment. This work steps away from feature-based approaches, hence employing Mutual Information (MI) as a similarity metric to estimate an alignment transformation. Moreover, high-resolution geometry and imagery are combined to cope with the fragility and severe damage of heritage fragments. Therefore, the testing data is composed of a set of ancient Egyptian decorated broken fragments recorded through 3D remote sensing techniques. More specifically, structured light technology for mesh models creation, as well as orthophotos, upon which digital drawings are created. Even though this study is restricted to Egyptian artifacts, the workflow can be applied to reconstruct different types of decoration patterns in the cultural heritage domain.

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Section: Related Workmentioning

confidence: 99%

A Generative and Entropy-Based Registration Approach for the Reassembly of Ancient Inscriptions

Lima-Hernandez

Vergauwen

2021

Remote Sensing

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“…The same approach is used in [84], where the features obtained by a variation of the AlexNet CNN are used to train an SVM, showing that automatically extracted features can outperform classical image descriptors. Other examples of style image classification can be found in [17,30,42,59].…”

Section: Related Work 21 Computer Vision and Deep Learning For Artwor...mentioning

confidence: 99%

A Data Set and a Convolutional Model for Iconography Classification in Paintings

Milani,

Fraternali

2020

Preprint

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Iconography in art is the discipline that studies the visual content of artworks to determine their motifs and themes and to characterize the way these are represented. It is a subject of active research for a variety of purposes, including the interpretation of meaning, the investigation of the origin and diffusion in time and space of representations, and the study of influences across artists and art works. With the proliferation of digital archives of art images, the possibility arises of applying Computer Vision techniques to the analysis of art images at an unprecedented scale, which may support iconography research and education. In this paper we introduce a novel paintings data set for iconography classification and present the quantitative and qualitative results of applying a Convolutional Neural Network (CNN) classifier to the recognition of the iconography of artworks. The proposed classifier achieves good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73% Average Precision) in the task of identifying saints in Christian religious paintings, a task made difficult by the presence of classes with very similar visual features. Qualitative analysis of the results shows that the CNN focuses on the traditional iconic motifs that characterize the representation of each saint and exploits such hints to attain correct identification. The ultimate goal of our work is to enable the automatic extraction, decomposition, and comparison of iconography elements to support iconographic studies and automatic art work annotation.

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“…[34] uses an online learning algorithm to classify painting images by using multi-features. [4] focuses on ancient paintings' chronological classification problems by extracting a uniform feature that can represent the multiview appearance and color attributes of objects and use this feature for ancient paintings chronological classification. [1] explores the problem of feature extraction on paintings and focuses on the classification of paintings into their genres and styles.…”

Section: Ancient Painting Processingmentioning

confidence: 99%

Ancient Painting to Natural Image: A New Solution for Painting Processing

Qiao

Zhang

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Collecting a large-scale and well-annotated dataset for image processing has become a common practice in computer vision. However, in the ancient painting area, this task is not practical as the number of paintings is limited and their style is greatly diverse. We, therefore, propose a novel solution for the problems that come with ancient painting processing. This is to use domain transfer to convert ancient paintings to photo-realistic natural images. By doing so, the "ancient painting processing problems" become "natural image processing problems" and models trained on natural images can be directly applied to the transferred paintings. Specifically, we focus on Chinese ancient flower, bird and landscape paintings in this work. A novel Domain Style Transfer Network (DSTN) is proposed to transfer ancient paintings to natural images which employ a compound loss to ensure that the transferred paintings still maintain the color composition and content of the input paintings. The experiment results show that the transferred paintings generated by the DSTN have a better performance in both the human perceptual test and other image processing tasks than other state-of-art methods, indicating the authenticity of the transferred paintings and the superiority of the proposed method.

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Multi-View Feature Combination for Ancient Paintings Chronological Classification

Cited by 18 publications

References 23 publications

A Generative and Entropy-Based Registration Approach for the Reassembly of Ancient Inscriptions

A Generative and Entropy-Based Registration Approach for the Reassembly of Ancient Inscriptions

A Data Set and a Convolutional Model for Iconography Classification in Paintings

Ancient Painting to Natural Image: A New Solution for Painting Processing

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