Compression ensembles quantify aesthetic complexity and the evolution of visual art

Karjus, Andres; Solà, Mar Canet; Ohm, Tillmann; Ahnert, Sebastian E.; Schich, Maximilian

doi:10.48550/arxiv.2205.10271

Cited by 4 publications

(4 citation statements)

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“…In future work, we aim to extend the StyleObject7K dataset and will include more images in it. Furthermore, we plan to extend our studies to include artwork images, such as a 65K benchmark abstract art of Art500K and WikiArt, as used in another stream of work within our research group [60]. Finally, we also plan to advance our investigation through the inclusion of more recent and emerging deep learning models.…”

Section: Discussionmentioning

confidence: 99%

Toward cross‐domain object detection in artwork images using improved YoloV5 and XGBoosting

Ahmad

Schich

2023

IET Image Processing

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Object recognition in natural images has achieved great success, while recognizing objects in style-images, such as artworks and watercolor images, has not yet achieved great progress. Here, this problem is addressed using cross-domain object detection in style-images, clipart, watercolor, and comic images. In particular, a cross-domain object detection model is proposed using YoloV5 and eXtreme Gradient Boosting (XGBoosting). As detecting difficult instances in cross domain images is a challenging task, XGBoosting is incorporated in this workflow to enhance learning of the proposed model for application on hard-to-detect samples. Several ablation studies are carried out by training and evaluating this model on the StyleObject7K, ClipArt1K, Watercolor2K, and Comic2K datasets. It is empirically established that this proposed model works better than other methods for the above-mentioned datasets.

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Section: Discussionmentioning

confidence: 99%

Toward cross‐domain object detection in artwork images using improved YoloV5 and XGBoosting

Ahmad

Schich

2023

IET Image Processing

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“…We treated each metadata dimension separately, but spaces can also be concatenated (and weighted, if necessary) to produce joint embeddings (cf. [105,106]).…”

Section: Future Researchmentioning

confidence: 99%

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Zemaityte¹,

Karjus²,

Rohn³

et al. 2023

Preprint

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Film festivals are a key component in the global film industry in terms of trendsetting, publicity, trade, and collaboration. We present an unprecedented analysis of the international film festival circuit, which has so far remained relatively understudied quantitatively, partly due to the limited availability of suitable data sets. We use large-scale data from the Cinando platform of the Cannes Film Market, widely used by industry professionals. We explicitly model festival events as a global network connected by shared films, and quantify festivals as aggregates of the metadata of their showcased films. Importantly, we argue against using simple count distributions for discrete labels such as language or production country, as such categories are typically not equidistant. Rather, we propose embedding them in continuous latent vector spaces. We demonstrate how these “festival embeddings” provide insight into changes in programmed content over time, predict festival connections, and can be used to measure diversity in film festival programming across various cultural, social, and geographical variables---which all constitute an aspect of public value creation by film festivals. Our results provide a novel mapping of the film festival circuit between 2009--2021 (616 festivals, 31,989 unique films), highlighting festival types that occupy specific niches, diverse series, and those that evolve over time. We also discuss how these quantitative findings fit into media studies and research on public value creation by cultural industries. With festivals occupying a central position in the film industry, investigations into the data they generate hold opportunities for researchers to better understand industry dynamics and cultural impact, and for organizers, policymakers, and industry actors to make more informed, data-driven decisions. We hope our proposed methodological approach to festival data paves way for more comprehensive film festival studies and large-scale quantitative cultural event analytics in general.

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“…With respect to other computational methods for the analysis of large-scale datasets, our dataset can be used to cross-validate other methods, such as work on aesthetic complexity [ 39 ]. Our dataset and methods can be employed for similar uses as outlined in [ 39 ], including being related to human judgments, evaluating individual artists’ trajectories over their careers, or being used for authorship and style attribution.…”

Section: An Information-theoretic Framework For Cultural Analysismentioning

confidence: 99%

WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures

Desikan

Shimao

Miton

2022

Entropy

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With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high quality images of historically significant artworks by over 3000 artists, ranging from the 15th century to the present day; it is a rich source for the potential mining of patterns and differences among artists, genres, and styles. However, such datasets are often difficult to analyse and use for answering complex questions of cultural evolution and divergence because of their raw formats as image files, which are represented as multi-dimensional tensors/matrices. Recent developments in machine learning, multi-modal data analysis and image processing, however, open the door for us to create representations of images that extract important, domain-specific features from images. Art historians have long emphasised the importance of art style, and the colors used in art, as ways to characterise and retrieve art across genre, style, and artist. In this paper, we release a massive vector-based dataset of paintings (WikiArtVectors), with style representations and color distributions, which provides cultural and social scientists with a framework and database to explore relationships across these two vital dimensions. We use state-of-the-art deep learning and human perceptual color distributions to extract the representations for each painting, and aggregate them across artist, style, and genre. These vector representations and distributions can then be used in tandem with information-theoretic and distance metrics to identify large-scale patterns across art style, genre, and artist. We demonstrate the consistency of these vectors, and provide early explorations, while detailing future work and directions. All of our data and code is publicly available on GitHub.

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Compression ensembles quantify aesthetic complexity and the evolution of visual art

Cited by 4 publications

References 5 publications

Toward cross‐domain object detection in artwork images using improved YoloV5 and XGBoosting

Toward cross‐domain object detection in artwork images using improved YoloV5 and XGBoosting

Quantifying the global film festival circuit: Networks, diversity, and public value creation

WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures

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