Titlebook: Data Analytics for Cultural Heritage; Current Trends and C Abdelhak Belhi,Abdelaziz Bouras,Abdul Hamid Sadka Book 2021 The Editor(s) (if ap

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https://doi.org/10.1057/9781137302823uated in terms of user’s experience. The evaluation results showed the effectiveness of the proposed framework in offering a high-quality visual experience with a speedy response time of the interaction system.

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NoisyArt: Exploiting the Noisy Web for Zero-shot Classification and Artwork Instance Recognition,iments demonstrate the benefits and limitations of this kind of approaches in the challenging setting of data scarcity and noisy labels for the set of seen classes. This chapter combines and extends our ongoing work on . dataset.

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challenges of improving data acquisition, enrichment and ma.This book considers the challenges related to the effective implementation of artificial intelligence (AI) and machine learning (ML) technologies to the cultural heritage digitization process. Particular focus is placed on improvements to

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Early Modern Literature in Historyication can be particularly challenging due to a high number of different image categories, feature variability, and the need for high reliability. Recent research shows that various machine learning techniques can be utilized for image classification purposes and that algorithms such as artificial

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‘An unnecessary flood of words’?ral heritage classification relies on the classification of asset images regarding a certain task such as type, artist, genre, style identification, etc. CH classification is challenging as various CH asset images have similar colors, textures, and shapes. In this chapter, the aim is to study and ev