WikiArt: An Ontology-Based Information Retrieval System for Arts

Pirrone, Roberto; Cannella, Vincenzo; Gambino, Orazio; Pipitone, Arianna; Russo, Giuseppe

doi:10.1109/isda.2009.219

Cited by 8 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roberto et.al. Illustrates the ontology related information retrieval system used for arts and it known as WikiArt [14]. A WikiArt is a system that able to generates the result by considering three types of the sources i.e.…”

Section: Consider the Examplementioning

confidence: 99%

A Heuristic Approach to Enforce String Transformation using Ontology and Log Module

Katre¹

2015

IJCA

View full text Add to dashboard Cite

Searching proper information is becoming most challenging task due to increasing amount of information in the web. Search engines smartly do this thing to fulfill the user"s requirement. But Search engines are packed with billions of URL"s and there are millions of permutation and combination of the keywords to provide the query for search engines. So, to ease this process of firing query where user can come to know about the query string as he is entering some consecutive characters for the query. And this is known as the String transformation technique. Many methods are been introduced to provide service for this technique, but most of them are not relay on the meaning of the String. So proposed system put forwards an idea where semantic of the word is identified using the ontology. Using generalized inverted index actually speed-up the process of searching.

show abstract

Section: Consider the Examplementioning

confidence: 99%

A Heuristic Approach to Enforce String Transformation using Ontology and Log Module

Katre¹

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…One such popular dataset is WikiArt , which contains nearly 250,000 artworks by 3000 artists, localized in eight languages, featuring artwork from over a 100 countries. While there exist other datasets that curate artworks for analysis, for example, the National Gallery of Art dataset, WikiArt ([ 1 , 2 ]) serves as an especially valuable resource for its extensive coverage of artworks over a millenium, annotated by artist, date, genre, and style. WikiArt ’s wide adoption in the computer-vision community, as well as its use in multi-modal datasets, such as Wiki Art Emotions [ 3 ], make it a valuable dataset for cultural analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Desikan

Shimao

Miton

2022

Entropy

View full text Add to dashboard Cite

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.

show abstract

DeepArtist: A Dual-Stream Network for Painter Classification of Highly-Varying Image Resolutions

Nevo

David

Netanyahu

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

WikiArt: An Ontology-Based Information Retrieval System for Arts

Cited by 8 publications

References 9 publications

A Heuristic Approach to Enforce String Transformation using Ontology and Log Module

A Heuristic Approach to Enforce String Transformation using Ontology and Log Module

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

DeepArtist: A Dual-Stream Network for Painter Classification of Highly-Varying Image Resolutions

Contact Info

Product

Resources

About