Acquisition of multimedia ontology: an application in preservation of cultural heritage

Mallik, Anupama; Chaudhury, Santanu

doi:10.1007/s13735-012-0021-5

Cited by 20 publications

(4 citation statements)

References 18 publications

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“…For instance, in content-based image retrieval, breaking down each image into smaller components when uploading them to a database allows for more refined search queries by clients. Similarly, in human-computer interaction, segmenting all elements within each video frame enables more effective interactions between the user and various persons or objects in the environment [59,60]. In logistics systems, image segmentation plays a crucial role in precisely identifying and isolating specific objects or items within an image.…”

Section: Deep Learningmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

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Section: Deep Learningmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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“…Toward the direction of intelligent reasoning, Mallik et al leveraged algorithmic recognition to denote performative hand gestures, facial expressions, and body postures in Indian classical dance. On this basis, acquiring heritage media into a specialized ontology became automated at both the knowledge and feature level [109]. Furthermore, [27] introduced the use of a probabilistic framework, namely Multi-Entity Bayesian Networks (MEBNs), for analyzing movements in dance with additional information.…”

Section: Encoding Motion In the Contextmentioning

confidence: 99%

Digitizing Intangible Cultural Heritage Embodied: State of the Art

Hou

Kenderdine

Picca

et al. 2022

J. Comput. Cult. Herit.

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Intangible cultural heritage (ICH) as a field of research and site for digital efforts has grown significantly since the UNESCO 2003 Convention for the Safeguarding of Intangible Heritage. In contrast to tangible heritage, where cultural identities are manifested through physical objects, intangible cultural expressions are defined through tacit reliances and embodied practices. Such practices are usually bodily communicated, enacted, socially transmitted, and constantly evolving. Burgeoning trends in computational heritage and ICT applications have played a crucial role in safeguarding ICH as they produce versatile resources while making them accessible to the public. Nevertheless, most of the inventions are object-centric and cater to conserving material-based knowledge bases. Few endeavors thus far have fully supported the recording, representing, and reviving of the living nature of ICH. One of the challenges now faced is to find appropriate forms, together with efficient methods, to document the ephemeral aspects of intangible heritage. Another barrier is to find effective ways to communicate the knowledge inextricably linked to people. In response, recent efforts have embarked on capturing the “live” and “active” facets of the embodied cultures, which entails addressing technological and curatorial complexity to communicate the material and immaterial aspects within a meaningful context. Meanwhile, advancements in experimental museology have opened up new modes of experiential narratives, particularly through visualization, augmentation, participation, and immersive embodiment. Novel practices of cultural data computation and data sculpting have also emerged toward the ideal of knowledge reconstruction. This article outlines state-of-the-art models, projects, and technical practices that have advanced the digitization lifecycle for ICH resources. The review focuses on several critical but less studied tasks within digital archiving, computational encoding, conceptual representation, and interactive engagement with the intangible cultural elements. We aim to identify the advancements and gaps in the existing conventions, and to envision opportunities for transmitting embodied knowledge in intangible heritage.

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“…In this way, the output labels of the neural network correspond exactly to the BIM categories and, once the geometry has been reconstructed, it will be possible to associate its information directly to the specific classes. In the current state of the art, some works have already associated semantics, based on taxonomies and ontologies, to heritage elements (Mallik and Chaudhury, 2012) or HBIM models (Quattrini et al, 2017;Yang et al, 2019). However, there are still no studies that combine the semantic of the BIM domain with the automatic recognition of architectural elements through the DL techniques.…”

Section: Class Definitionmentioning

confidence: 99%

A Benchmark for Large-Scale Heritage Point Cloud Semantic Segmentation

Matrone

Lingua

Pierdicca

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.polito.it/, comprises datasets and classification results for better comparisons and insights into the strengths and weaknesses of different machine and deep learning approaches for heritage point cloud semantic segmentation, in addition to promoting a form of crowdsourcing to enrich the already annotated database.

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Acquisition of multimedia ontology: an application in preservation of cultural heritage

Cited by 20 publications

References 18 publications

A Systematic Literature Review on the Application of Automation in Logistics

A Systematic Literature Review on the Application of Automation in Logistics

Digitizing Intangible Cultural Heritage Embodied: State of the Art

A Benchmark for Large-Scale Heritage Point Cloud Semantic Segmentation

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