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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‘An unnecessary flood of words’?ataset of 17 ICH categories and manually annotate them. We start with fine-tuning recent pre-trained deep learning models such as VGG19, ResNet50, Inception-v3, and Xception for classifying our own dataset. Followed which, we propose to train support vector machine (SVM) models using many popular vi

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‘An unnecessary flood of words’? are recognized by UNESCO. We need to preserve the information relevant to the sites in terms of history, art, culture, materials, architecture styles, and their role in the socioeconomic growth. These sites are of interest and value to architects, historians, and tourists for various levels of expl

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Sabrina P. Ramet,Ola Listhaug,Albert Simkusgy based on stochastic mathematics is applied for the quantification of aesthetic attributes of paintings and landscapes. The paintings analyzed include Da Vinci, Pablo Picasso, and various other celebrated paintings from 1250?AD to modern times. In regard to landscapes, the analysis focuses on the

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Understanding the Ohrid Framework Agreementn visual arts is to find similarity relationships among paintings of different artists and painting schools. To help art historians better understand visual arts, this chapter presents a framework for . in digital painting datasets. The proposed framework is based, on one hand, on a deep convolution

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