The Rijksmuseum Challenge

Mensink, Thomas; Gemert, Jan C. van

doi:10.1145/2578726.2578791

Cited by 96 publications

(16 citation statements)

References 14 publications

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“…Fortunately the appearance of large, annotated and online available fine art collections such as the WikiArt 1 dataset, which contains more than 130k artwork images, enabled the adoption of deep learning techniques, as well as helped shaping a more uniform framework for method comparison. To the best of our knowledge, the WikiArt dataset is currently the most commonly used dataset for art-related classifications tasks (Karayev et al, 2014;Bar et al, 2014;David & Netanyahu, 2016;Girshick et al, 2014;Hentschel et al, 2016;Seguin et al, 2016;Chu & Wu, 2016;, even though other online available sources are also being used such as the Web Gallery of Art 2 (WGA) with more than 40k images (Seguin et al, 2016); or the Rijksmuseum challenge dataset (van Noord et al, 2015;Mensink & Van Gemert, 2014). Furthermore, there were several initiatives for building painting datasets dedicated primarily to fine art image classification such as Painting-91 (Khan et al, 2014), which consists of 4266 images from 91 different painters; the Pandora dataset consisting of 7724 images from 12 art movements (Florea et al, 2016) and the recently introduced museum-centric OmniART dataset with more than 1M photographic reproductions of artworks (Strezoski & Worring, 2017).…”

Section: Related Workmentioning

confidence: 99%

Fine-tuning Convolutional Neural Networks for fine art classification

Cetinić

Lipić

Grgić

2018

Expert Systems with Applications

190

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The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections.

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Section: Related Workmentioning

confidence: 99%

Fine-tuning Convolutional Neural Networks for fine art classification

Cetinić

Lipić

Grgić

2018

Expert Systems with Applications

190

View full text Add to dashboard Cite

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“…The fact that the migration of patterns in paintings is mainly important in the Modern Period (1400-1800) is an important factor in choosing our base [12,13] for object classification. -RKD Challenge [25]: coming from the Rijksmuseum, this benchmark was created for scientists to test their algorithms on artists identification, labelling of materials and estimating the creation year. Boasting 112k elements, only 3'600 are actual paintings.…”

Section: Choice Of the Base Corpusmentioning

confidence: 99%

Visual Link Retrieval in a Database of Paintings

Séguin

Striolo

diLenardo

et al. 2016

Lecture Notes in Computer Science

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Abstract. This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pretrained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task.

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“…• KNN (K-Nearest Neighbors) [6,19,21,22,30]: this is a statistical method for predicting new input by calculating the similarity between the test data and the new instance by locating the closest data points (or data objects) in the training dataset based on certain distance functions. K denotes the number of closest data points (i.e., neighbors).…”

Section: Algorithms Of ML For Chmentioning

confidence: 99%

An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

Nafis¹,

Fararni²,

Yahyaouy³

et al. 2021

IJACSA

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The Scientific Cultural Heritage (SCH) of the Drâa-Tafilalet region in south-eastern Morocco is a rich source of data testifying to the ingenuity of an older generation that has shaped the past of the region. These data must be preserved for future generations, particularly with new technologies and the semantic web. Recommendation systems (RS) are intended to assist prospective users in recommending the most suitable services based on their profile and expectations. The collaborative filtering (CF), content filtering (CB) or hybrid filtering (CF) RS has shown promising results in order to explore the problems experienced especially in CH. However, there are some limitations to be resolved, mostly due to the ability of these methods to build a stable and complete framework, which can provide a complete image of the user profile and suggest the most appropriate offers. This paper presents a hybrid recommender system for SCH data; a field little explored despite its historical importance and the value it generates. The results presented in this paper belong to the data collected from the region of Drâa-Tafilalet in southern Morocco.

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The Rijksmuseum Challenge

Cited by 96 publications

References 14 publications

Fine-tuning Convolutional Neural Networks for fine art classification

Fine-tuning Convolutional Neural Networks for fine art classification

Visual Link Retrieval in a Database of Paintings

An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

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