Saving Cultural Heritage with Digital Make-Believe: Machine Learning and Digital Techniques to the Rescue

M, Yasser, A,; Clawson, Kathy; Bowerman, Chris; Lévêque, M

doi:10.14236/ewic/hci2017.97

Cited by 23 publications

(10 citation statements)

References 7 publications

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“…Cultural heritage is a large area where cultural sensitivity needs to be taken into account, and where new challenges appear with digital technologies. For instance, on one hand, digital technologies can offer access to historical sites which would be hard to enter or are prone to damage if visited [16]. On the other hand, exposing these sites can be perceived as an intrusion, e.g., if the site is considered as sacred, or carries a strong cultural or religious meaning.…”

Section: Related Workmentioning

confidence: 99%

Design Sensibilities - Designing for Cultural Sensitivity

Häkkilä

Wiberg

Eira

et al. 2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

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When designing interactive systems and applications, we need to take into account different user groups, contexts, and cultural sensitivities. The challenges in culturally sensitive design can arise e.g. from historical, ideological, or ethical factors, and need to be considered when conducting HCI research, e.g. with cultural heritage, under-represented user groups, topics of cultural rituals, or in cross-cultural interfaces. The digitalizing world crosses old borders by bringing technology connectivity for new domains, and provides the means to distribute information that has previously been harder to access, and which may contain strong cultural meanings. With emerging technology use, new etiquette and social practices are formed to reflect the new sub-cultures. This workshop addresses the cultural sensitivities when designing interactive systems. The workshop invites researchers and practitioners to present and discuss about related case studies, applications, research methods, and experiences. CCS CONCEPTS • Human-centered computing → Empirical studies in collaborative and social computing.

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Section: Related Workmentioning

confidence: 99%

Design Sensibilities - Designing for Cultural Sensitivity

Häkkilä

Wiberg

Eira

et al. 2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

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“…In [25], the authors classify images of Mexican buildings into three different architectural styles with GoogleNet [32] and AlexNet [16]. In [41], a small dataset comprising 432 images corresponding to 4 regions of cultural interest is collected from Flickr and then the image labels are predicted by the fine-tuned AlexNet. This work demonstrates that art type prediction is more accurate using the fine-tuned AlexNet convolutional neural network (CNN) architecture than when implementing a support vector machine (SVM) with SIFT features [24].…”

Section: Related Work 21 Multi-class and Multi-label Classificationmentioning

confidence: 99%

Generating Captions for Images of Ancient Artworks

Sheng

Moens

2019

Proceedings of the 27th ACM International Conference on Multimedia

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The neural encoder-decoder framework is widely adopted for image captioning of natural images. However, few works have contributed to generating captions for cultural images using this scheme. In this paper, we propose an artwork type enriched image captioning model where the encoder represents an input artwork image as a 512-dimensional vector and the decoder generates a corresponding caption based on the input image vector. The artwork type is first predicted by a convolutional neural network classifier and then merged into the decoder. We investigate multiple approaches to integrate the artwork type into the captioning model among which is one that applies a step-wise weighted sum of the artwork type vector and the hidden representation vector of the decoder. This model outperforms three baseline image captioning models for a Chinese art image captioning dataset on all evaluation metrics. One of the baselines is a state-of-the-art approach fusing textual image attributes into the captioning model for natural images. The proposed model also obtains promising results for another Egyptian art image captioning dataset. CCS CONCEPTS • Information systems → Multimedia content creation; • Computing methodologies → Scene understanding.

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“…On the external facades, there are 135 spires and 30 decorative reverse arches that seem to support the flanks of the impressive building. A popular DL approach relies on convolutional neural networks (CNN) that recombine sets of neurons in different layers, in order to process a set of input vectors to a known set of outputs [35,36]. PointNet [37], and its later improvement PointNet++ [38], is a unified architecture that learns both global and local point features and is suitable to perform classification, part segmentation, and semantic scene segmentation.…”

Section: Milan Cathedralmentioning

confidence: 99%

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

et al. 2020

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The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

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Saving Cultural Heritage with Digital Make-Believe: Machine Learning and Digital Techniques to the Rescue

Cited by 23 publications

References 7 publications

Design Sensibilities - Designing for Cultural Sensitivity

Design Sensibilities - Designing for Cultural Sensitivity

Generating Captions for Images of Ancient Artworks

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

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