Computer Vision and Computer Graphics Analysis of Paintings and Drawings: An Introduction to the Literature

Stork, David G.

doi:10.1007/978-3-642-03767-2_2

Cited by 75 publications

(44 citation statements)

References 87 publications

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“…The area of art image retrieval has attracted the attention of researchers in the fields of computer vision and machine learning [27,33,42]. The main focus of the papers is on the artistic identification problem, where the goal is to classify original and fake paintings of a given artist [4,32,39] or to produce stylistic analysis of paintings [20,24,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This loss of visual information reduces the effectiveness of current image analysis techniques, which usually work with photographic digital images [14]. Art image analysis methodologies can also be used for art prints [42,27], but the great majority of these techniques have been developed for the analysis of digitized images of paintings, which contain richer visual information than prints. However, note that Li and Wang [29] have developed a system that analyzes ancient Chinese paintings, which are similar to art prints.…”

Section: Introductionmentioning

confidence: 99%

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Graph-based methods for the automatic annotation and retrieval of art prints

Carneiro

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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The analysis of images taken from cultural heritage artifacts is an emerging area of research in the field of information retrieval. Current methodologies are focused on the analysis of digital images of paintings for the tasks of forgery detection and style recognition. In this paper, we introduce a graph-based method for the automatic annotation and retrieval of digital images of art prints. Such method can help art historians analyze printed art works using an annotated database of digital images of art prints. The main challenge lies in the fact that art prints generally have limited visual information. The results show that our approach produces better results in a weakly annotated database of art prints in terms of annotation and retrieval performance compared to state-of-the-art approaches based on bag of visual words.H.3.3Information storage and retrievalInformation search and retrieval|Retrieval models I.4.8Image Processing and Computer VisionScene Analysis|Object Recognition G.3Probability and StatisticsProbabilistic Algorithms General TermsAlgorithms, Measurement, Experimentation Keywords Content-based image retrieval and annotation, Art image annotation and retrieval, Graph-based Learning methods

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph-based methods for the automatic annotation and retrieval of art prints

Carneiro

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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“…Image processing techniques for the automatic or semiautomatic analysis of paintings represent a valuable tool for supporting a number of challenging tasks, ranging from painting classification, comparison, and search and retrieval, to artist identification, artwork dating, and authentication [11].…”

Section: Introductionmentioning

confidence: 99%

Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible features from van Gogh’s works

2014

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The automatic extraction of objective features from paintings, like brushstroke distribution, orientation, and shape, could be particularly useful for different artwork analyses and management tasks. In fact, these features contribute to provide a unique signature of the artists' style and can be effectively used for artist identification and classification, artwork examination and retrieval, etc. In this paper, an automatic technique for unsupervised extraction of individual brushstrokes from digital reproductions of van Gogh's paintings is presented. Through the iterative application of segmentation, characterization, and validation steps, valid brushstrokes complying with specific area and shape constraints are identified. On the extracted brushstrokes, several representative features can then be calculated, like orientation, length, and width. The accuracy of the devised method is evaluated by comparing numerical results obtained on a dataset of digital reproductions of van Gogh's oil-on-canvas works with observations made by human subjects and with another recent approach for automatic brushstroke analysis. Experimental tests showed that the devised methodology produces results that are rather close to those obtained by human subjects and, for some of the metrics considered, can provide improved performances with respect to alternative techniques.

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“…Recently, an increasing community of researchers in computer vision, pattern recognition, image processing and art history have developed rigorous computer methods for addressing an increasing number of problems in the history of art [1]. In our case, the images used by the sculptor Miquel Planas [2] constitute an essential part of his creative process and he presents them as a large document collection on which subsequent work will be based, especially in the field of sculpture.…”

Section: Introductionmentioning

confidence: 99%

Semantic-Based Image Analysis with the Goal of Assisting Artistic Creation

Rosado

Reverter

Figueras

et al. 2014

Computer Vision and Graphics

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Abstract. We have approached the difficulties of automatic cataloguing of images on which the conception and design of sculptor M. Planas artistic production are based. In order to build up a visual vocabulary for basing image description on, we followed a procedure similar to the method Bag-of-Words (BOW). We have implemented a probabilistic latent semantic analysis (PLSA) that detects underlying topics in images. Whole image collection was clustered into different types that describe aesthetic preferences of the artist. The outcomes are promising, the described cataloguing method may provide new viewpoints for the artist in future works.

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Computer Vision and Computer Graphics Analysis of Paintings and Drawings: An Introduction to the Literature

Cited by 75 publications

References 87 publications

Graph-based methods for the automatic annotation and retrieval of art prints

Graph-based methods for the automatic annotation and retrieval of art prints

Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible features from van Gogh’s works

Semantic-Based Image Analysis with the Goal of Assisting Artistic Creation

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