Classification of Moroccan Decorative Patterns Based on Machine Learning Algorithms

Aoulalay, Ayoub; Makhfi, Noureddine El; Abounaima, Mohammed Chaouki; Massar, Mohammed

doi:10.1109/icecocs50124.2020.9314443

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…These data are stored in the database together with the material data, which is convenient for the verifier to query and analyze quickly, which is also conducive to the realization of more efficient and accurate dating. In the same year, Aoulalay et al [ 15 ] applied statistical learning theory and support vector machine methods to determine the origin of ancient ceramics when studying the processing of limited sample sets of trace elements and achieved good results. Wang et al [ 16 ] used the main chemical composition of the porcelain body as the classification basis and proposed the application of the gray relational analysis method to the classification of porcelain and gave the classification effect of these two types of porcelain.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

2021

Computational Intelligence and Neuroscience

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The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

2021

Computational Intelligence and Neuroscience

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“…In another study, Beatini [140] investigated the conversion of IGPs to kinetic rosette patterns through modular linkages that rely on the same type and number of symmetry operations as the reference models. In addition, Aoulalay et al classified Islamic Moroccan decorative patterns [141] and more IGPs [142] based on machine learning algorithms through feature extraction. Hajebi and Hajebi [143] proposed a new method for the intelligent restoration of the parametric IGPs.…”

Section: Aesthetic-based; Generation Methods and Variationsmentioning

confidence: 99%

Application-based principles of islamic geometric patterns; state-of-the-art, and future trends in computer science/technologies: a review

Azari

Bemanian²,

Mahdavinejad³

et al. 2023

Herit Sci

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Currently, there is a tendency to use Islamic Geometric Patterns (IGPs) as important identities and cultural elements of building design in the Middle East. Despite high demand, lack of information about the potential of IGPs principles have led to formal inspiration in the design of existing buildings. Many research studies have been carried out on the principles of IGPs. However, comprehensive studies relating to new possibilities, such as structure-based, sustainable-based, and aesthetic-based purposes, developed by computer science and related technologies, are relatively rare. This article reviews the state-of-the-art knowledge of IGPs, provides a survey of the main principles, presents the status quo, and identifies gaps in recent research directions. Finally, future prospects are discussed by focussing on different aspects of the principles in accordance with collected evidence obtained during the review process.

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“…hieroglyphs [40][41][42]; and deciphering the text of carbonised scrolls [43] and the authorship attributions of handwritten manuscripts [35,44,45]. In the broader field of material culture, AI systems have been used for the classification and/or reconstruction of pottery [46][47][48][49][50][51][52][53][54][55], and provenance studies of pottery [55], as well as the classification of ancient coins [56], constituent materials in shell middens [57], or of cut marks on bones retrieved from archaeological sites sites [58,59]. Custom -designed AI systems have also been used in the field of restoration and conservation [55,60,61].…”

Section: Use Of Genai Language Models In Cultural Heritage Studiesmentioning

confidence: 99%

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Spennemann

2024

Heritage

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Generative artificial intelligence (genAI) language models have become firmly embedded in public consciousness. Their abilities to extract and summarise information from a wide range of sources in their training data have attracted the attention of many scholars. This paper examines how four genAI large language models (ChatGPT, GPT4, DeepAI, and Google Bard) responded to prompts, asking (i) whether artificial intelligence would affect how cultural heritage will be managed in the future (with examples requested) and (ii) what dangers might emerge when relying heavily on genAI to guide cultural heritage professionals in their actions. The genAI systems provided a range of examples, commonly drawing on and extending the status quo. Without a doubt, AI tools will revolutionise the execution of repetitive and mundane tasks, such as the classification of some classes of artifacts, or allow for the predictive modelling of the decay of objects. Important examples were used to assess the purported power of genAI tools to extract, aggregate, and synthesize large volumes of data from multiple sources, as well as their ability to recognise patterns and connections that people may miss. An inherent risk in the ‘results’ presented by genAI systems is that the presented connections are ‘artifacts’ of the system rather than being genuine. Since present genAI tools are unable to purposively generate creative or innovative thoughts, it is left to the reader to determine whether any text that is provided by genAI that is out of the ordinary is meaningful or nonsensical. Additional risks identified by the genAI systems were that some cultural heritage professionals might use AI systems without the required level of AI literacy and that overreliance on genAI systems might lead to a deskilling of general heritage practitioners.

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Classification of Moroccan Decorative Patterns Based on Machine Learning Algorithms

Cited by 5 publications

References 20 publications

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

Application-based principles of islamic geometric patterns; state-of-the-art, and future trends in computer science/technologies: a review

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

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