Sifting through visual arts collections

Pflüger, Hermann; Ertl, T.

doi:10.1016/j.cag.2016.03.009

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Experiments that compare global image feature-based classifications with local image feature (and corresponding spatial distribution)-based classifications are reported in refs. [ 41 , 43 , 44 ] show that the performance of LadeCA’s algorithms is sufficient for interactive interfaces of the type described in this paper.…”

Section: Words In Ladecamentioning

confidence: 92%

“…), we can see refs. [ 7 , 28 , 41 – 44 ]. Experiments that compare global image feature-based classifications with local image feature (and corresponding spatial distribution)-based classifications are reported in refs.…”

Section: Words In Ladecamentioning

confidence: 99%

“…Clustering: Words with a large number of associated images can be clustered [ 44 ]. The cluster process creates subsets of related images from the image set of a given word.…”

Section: Words In Ladecamentioning

confidence: 99%

“…[ 7 ]). All images and representatives are shown side-by-side in their entirety [ 54 ] and not in an abstract manner, such as a point cloud [ 44 ]. Additional information is given separately [ 55 ] (blue lines that indicate relatedness) or in separate views or frames.…”

Section: Communications With Ladecamentioning

confidence: 99%

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A language to analyze, describe, and explore collections of visual art

Pflüger

2021

Vis. Comput. Ind. Biomed. Art

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A vast quantity of art in existence today is inaccessible to individuals. If people want to know the different types of art that exist, how individual works are connected, and how works of art are interpreted and discussed in the context of other works, they must utilize means other than simply viewing the art. Therefore, this paper proposes a language to analyze, describe, and explore collections of visual art (LadeCA). LadeCA combines human interpretation and automatic analyses of images, allowing users to assess collections of visual art without viewing every image in them. This paper focuses on the lexical base of LadeCA. It also outlines how collections of visual art can be analyzed, described, and explored using a LadeCA vocabulary. Additionally, the relationship between LadeCA and indexing systems, such as ICONCLASS or AAT, is demonstrated, and ways in which LadeCA and indexing systems can complement each other are highlighted.

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Section: Words In Ladecamentioning

confidence: 92%

Section: Words In Ladecamentioning

confidence: 99%

“…Clustering: Words with a large number of associated images can be clustered [ 44 ]. The cluster process creates subsets of related images from the image set of a given word.…”

Section: Words In Ladecamentioning

confidence: 99%

Section: Communications With Ladecamentioning

confidence: 99%

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A language to analyze, describe, and explore collections of visual art

Pflüger

2021

Vis. Comput. Ind. Biomed. Art

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Foreword to special section on SIGGRAD 2015

Peters

Doggett

Kjelldahl

2016

Computers & Graphics

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Deep Learning Algorithms-based Object Detection and Localization Revisited

Waheed

Suaib

Rahim

et al. 2021

J. Phys.: Conf. Ser.

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The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV.

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Sifting through visual arts collections

Cited by 8 publications

References 16 publications

A language to analyze, describe, and explore collections of visual art

A language to analyze, describe, and explore collections of visual art

Foreword to special section on SIGGRAD 2015

Deep Learning Algorithms-based Object Detection and Localization Revisited

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