Object Classification in Images of Neoclassical Furniture Using Deep Learning

Bermeitinger, Bernhard; Freitas, André; Donig, Simon; Handschuh, Siegfried

doi:10.1007/978-3-319-46224-0_10

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…, 2017; Tanasescu et al. , 2018), image and object classification (Bermeitinger et al. , 2016; Wevers and Smits, 2020), to more particular applications like Egyptian hieroglyphs recognition, classification and translation (Barucci et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Neural networks and deep learning techniques are among the most current approaches, enabling DH researchers to tackle demanding NLP and CV tasks. Examples range from more traditional use cases such as text analysis from historic and contemporary corpora (Clanuwat et al, 2019;Kestemont et al, 2017;Tanasescu et al, 2018), image and object classification (Bermeitinger et al, 2016;Wevers and Smits, 2020), to more particular applications like Egyptian hieroglyphs recognition, classification and translation (Barucci et al, 2021) or the development of semantic analysis and comparative query of art-historic collections (Garcia and Vogiatzis, 2019;Jain et al, 2021;Springstein et al, 2021). Gefen et al (2021) caution against the intrinsic disruptiveness of AI, which might deeply impact the way we understand, approach and produce cultural knowledge (p. 196).…”

Section: Ai Technology and Digital Archival Expertisementioning

confidence: 99%

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“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

Cushing

Osti²

2022

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PurposeThis study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.Design/methodology/approachIn this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.FindingsFour main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.Research limitations/implicationsThe volunteer basis of this study meant that the sample was not representative or generalisable.Originality/valueAlthough the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Ai Technology and Digital Archival Expertisementioning

confidence: 99%

“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

Cushing

Osti²

2022

View full text Add to dashboard Cite

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Spectrometry analysis based on approximation coefficients and deep belief networks

Tang

Gong

et al. 2018

NUCL SCI TECH

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Multi-Scale Feature Fusion for Interior Style Detection

et al. 2022

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Text-based search engines can extract various types of information when a user enters an appropriate search query. However, a text-based search often fails in image retrieval when image understanding is needed. Deep learning (DL) is often used for image task problems, and various DL methods have successfully extracted visual features. However, as human perception differs for each individual, a dataset with an abundant number of images evaluated by human subjects is not available in many cases, although DL requires a considerable amount of data to estimate space ambiance, and the DL models that have been created are difficult to understand. In addition, it has been reported that texture is deeply related to space ambiance. Therefore, in this study, bag of visual words (BoVW) is used. By applying a hierarchical representation to BoVW, we propose a new interior style detection method using multi-scale features and boosting. The multi-scale features are created by combining global features from BoVW and local features that use object detection. Experiments on an image understanding task were conducted on a dataset consisting of room images with multiple styles. The results show that the proposed method improves the accuracy by 0.128 compared with the conventional method and by 0.021 compared with a residual network. Therefore, the proposed method can better detect interior style using multi-scale features.

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Object Classification in Images of Neoclassical Furniture Using Deep Learning

Cited by 4 publications

References 8 publications

“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

Spectrometry analysis based on approximation coefficients and deep belief networks

Multi-Scale Feature Fusion for Interior Style Detection

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