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Relevant bibliographies by topics / Artwork Recognition

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Journal articles

Academic literature on the topic 'Artwork Recognition'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Artwork Recognition"

1

Wu, Tianyou. "Art Product Recognition Model Design and Construction of VR Model." Security and Communication Networks 2022 (June 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/3994102.

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The artwork embodies a profound human history and carries the essence of human civilization. Its content is complex and covers a wide range. How to use advanced technology to quickly and accurately classify and retrieve is an important research topic in the field. In our study, we first according to the requirements of practical application scenarios and existing data conditions proposed an overall scheme of artwork identification and retrieval. Through the functional analysis of the software required and the comparison of various databases, we present the system architecture design and data c

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2

Theodosiou, Zenonas, Marios Thoma, Harris Partaourides, and Andreas Lanitis. "A Systematic Approach for Developing a Robust Artwork Recognition Framework Using Smartphone Cameras." Algorithms 15, no. 9 (2022): 305. http://dx.doi.org/10.3390/a15090305.

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The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual env

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3

Yang, Heekyung, Jongdae Han, and Kyungha Min. "Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach." Sensors 19, no. 24 (2019): 5533. http://dx.doi.org/10.3390/s19245533.

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Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords

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4

Tian, Tian, and Feng Nan. "A Multitask Convolutional Neural Network for Artwork Appreciation." Mobile Information Systems 2022 (April 14, 2022): 1–8. http://dx.doi.org/10.1155/2022/8804711.

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The computational aesthetics of pictorial art is an important part of human artistic creation, and the computational aesthetics of pictorial art images is a computationally computable human aesthetic process using machines, which has important applications and scientific significance in the automated analysis of large-scale paintings and the computational modeling of perception by machines. To this end, this paper proposes a multitask convolutional neural network model for emotion and rating of artworks. (1) An artwork appreciation dataset consisting of fifty Chinese paintings and fifty Wester

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5

Jiang, Dayou, and Jongweon Kim. "Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection." International Journal of Machine Learning and Computing 8, no. 1 (2018): 54–60. http://dx.doi.org/10.18178/ijmlc.2018.8.1.663.

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6

Brossman, Craig, and George R. Cross. "Model-based recognition of characters in trademark artwork." Pattern Recognition Letters 11, no. 5 (1990): 363–70. http://dx.doi.org/10.1016/0167-8655(90)90046-5.

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7

Thomas, KErstin. "The Still Life of Objects – Heidegger, Schapiro, and Derrida reconsidered." eitschrift für Ästhetik und Allgemeine Kunstwissenschaft Band 60. Heft 1 60, no. 1 (2015): 81–102. http://dx.doi.org/10.28937/1000106256.

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Kerstin Thomas revaluates the famous dispute between Martin Heidegger, Meyer Schapiro, and Jacques Derrida, concerning a painting of shoes by Vincent Van Gogh. The starting point for this dispute was the description and analysis of things and artworks developed in his essay, “The Origin of the Work of Art”. In discussing Heidegger’s account, the art historian Meyer Schapiro’s main point of critique concerned Heidegger’s claim that the artwork reveals the truth of equipment in depicting shoes of a peasant woman and thereby showing her world. Schapiro sees a striking paradox in Heidegger’s claim

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8

Cetinic, Eva. "Towards Generating and Evaluating Iconographic Image Captions of Artworks." Journal of Imaging 7, no. 8 (2021): 123. http://dx.doi.org/10.3390/jimaging7080123.

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To automatically generate accurate and meaningful textual descriptions of images is an ongoing research challenge. Recently, a lot of progress has been made by adopting multimodal deep learning approaches for integrating vision and language. However, the task of developing image captioning models is most commonly addressed using datasets of natural images, while not many contributions have been made in the domain of artwork images. One of the main reasons for that is the lack of large-scale art datasets of adequate image-text pairs. Another reason is the fact that generating accurate descripti

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9

Iliadis, Lazaros, Spyridon Nikolaidis, Panagiotis Sarigiannidis, Shaohua Wan, and Sotirios Goudos. "Artwork Style Recognition Using Vision Transformers and MLP Mixer." Technologies 10, no. 1 (2021): 2. http://dx.doi.org/10.3390/technologies10010002.

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Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-

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10

Del Chiaro, Riccardo, Andrew D. Bagdanov, and Alberto Del Bimbo. "Webly-supervised zero-shot learning for artwork instance recognition." Pattern Recognition Letters 128 (December 2019): 420–26. http://dx.doi.org/10.1016/j.patrec.2019.09.027.

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