Relevant bibliographies by topics / Artwork Recognition

Academic literature on the topic 'Artwork Recognition'

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Published: 10 March 2023

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Journal articles on the topic "Artwork Recognition"

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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 conceptual design, and complete the system-level planning and design. Then, the crawler grabbing process is designed to obtain artwork graphic data, the artwork dataset production process and labeling status required for the target application scenario were introduced, and the category imbalance state of the target dataset was analyzed. Moreover, the database table structure design of the artwork identification and retrieval system, design and development of each functional module of the server, and the web client was introduced. Finally, according to the organization, structure, and characteristics of virtual reality system, a product design evaluation system based on virtual reality technology was constructed. A theoretical model VR-PDES was designed for the application of virtual reality technology in product design evaluation. The results of this research are of great significance for people to search for images of unknown artworks and improve the service capabilities and service levels of scenic spots.
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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 (August 27, 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 environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.
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Yang, Heekyung, Jongdae Han, and Kyungha Min. "Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach." Sensors 19, no. 24 (December 14, 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 and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.
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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 Western oil paintings was created, and twenty subjects were recruited to score the art appreciation of one hundred artworks in the dataset, covering both painting aesthetic evaluation and painting emotion evaluation. (2) Based on the artwork art appreciation dataset, an AlexNet-based convolutional neural network model is proposed to utilize the powerful feature extraction and classification capabilities of neural networks to complete artwork art appreciation, and an oversampling method and multitask learning method are used to improve the overall recognition accuracy. (3) Compared with the combination of traditional manual features + machine learning algorithms, the end-to-end multitask convolutional neural network proposed in this paper has the highest accuracy rate of 74.57%/71.43%/74.12%.
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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 (February 2018): 54–60. http://dx.doi.org/10.18178/ijmlc.2018.8.1.663.

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

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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 for truth, based on a specific object in a specific artwork while at the same time following a rather metaphysical idea of the artwork. Kerstin Thomas proposes an interpretation, which exceeds the common confrontation of philosophy versus art history by focussing on the respective notion of facticity at stake in the theoretical accounts of both thinkers. Schapiro accuses Heidegger of a lack of concreteness, which he sees as the basis for every truth claim on objects. Thomas understands Schapiro’s objections as motivated by this demand for a facticity, which not only includes the work of art, but also investigator in his concrete historical perspective. Truth claims under such conditions of facticity are always relative to historical knowledge, and open to critical intervention and therefore necessarily contingent. Following Thomas, Schapiro’s critique shows that despite his intention of giving the work of art back its autonomy, Heidegger could be accused of achieving quite the opposite: through the abstraction of the concrete, the factual, and the given to the type, he actually sets the self and the realm of knowledge of the creator as absolute and not the object of his knowledge. Instead, she argues for a revaluation of Schapiro’s position with recognition of the arbitrariness of the artwork, by introducing the notion of factuality as formulated by Quentin Meillassoux. Understood as exchange between artist and object in its concrete material quality as well as with the beholder, the truth of painting could only be shown as radically contingent. Thomas argues that the critical intervention of Derrida who discusses both positions anew is exactly motivated by a recognition of the contingent character of object, artwork and interpretation. His deconstructive analysis can be understood as recognition of the dynamic character of things and hence this could be shown with Meillassoux to be exactly its character of facticity – or factuality.
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Cetinic, Eva. "Towards Generating and Evaluating Iconographic Image Captions of Artworks." Journal of Imaging 7, no. 8 (July 23, 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 descriptions of artwork images is particularly challenging because descriptions of artworks are more complex and can include multiple levels of interpretation. It is therefore also especially difficult to effectively evaluate generated captions of artwork images. The aim of this work is to address some of those challenges by utilizing a large-scale dataset of artwork images annotated with concepts from the Iconclass classification system. Using this dataset, a captioning model is developed by fine-tuning a transformer-based vision-language pretrained model. Due to the complex relations between image and text pairs in the domain of artwork images, the generated captions are evaluated using several quantitative and qualitative approaches. The performance is assessed using standard image captioning metrics and a recently introduced reference-free metric. The quality of the generated captions and the model’s capacity to generalize to new data is explored by employing the model to another art dataset to compare the relation between commonly generated captions and the genre of artworks. The overall results suggest that the model can generate meaningful captions that indicate a stronger relevance to the art historical context, particularly in comparison to captions obtained from models trained only on natural image datasets.
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Iliadis, Lazaros, Spyridon Nikolaidis, Panagiotis Sarigiannidis, Shaohua Wan, and Sotirios Goudos. "Artwork Style Recognition Using Vision Transformers and MLP Mixer." Technologies 10, no. 1 (December 28, 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-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies.
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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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Dissertations / Theses on the topic "Artwork Recognition"

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Del, Chiaro Riccardo. "Anthropomorphous Visual Recognition: Learning with Weak Supervision, with Scarce Data, and Incrementally over Transient Tasks." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238101.

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In the last eight years the computer vision field has experienced dramatic improvements thanks to the widespread availability of data and affordable parallel computing hardware like GPUs. These two factors have contributed to making possible the training of very deep neural network models in reasonable times using millions of labeled examples for supervision. Humans do not learn concepts in this way. We do not need a massive number of labeled examples to learn new concepts; instead we rely on a few (or even zero) examples, infer missing information, and generalize. Moreover, we retain previously learned concepts without the need to re-train. We can easily ride a bicycle after years of not doing so, or recognize an elephant even though we may not have seen one recently. These characteristics of human learning, in fact, stand in stark contrast to how deep models learn: they require massive amounts of labeled data for training due to overparameterization, they have limited generalization capabilities, and they easily forget previously learned tasks or concepts when trained on new ones. These characteristics limit the applicability of deep learning in some scenarios in which these problems are more evident. In this thesis we study some of these and propose strategies to overcome some of the negative aspect of deep neural network training. We still use the gradient-based learning paradigm, but we adapt it to address some of these differences between human learning and learning in deep networks. Our goal is to achieve better learning characteristics and improve performance in some specific applications. We first study the artwork instance recognition problem, for which it is very difficult to collect large collections of labeled images. Our proposed approach relies on web search engines to collect examples, which results in the two related problems of domain shift due to biases in search engines and noisy supervision. We propose several strategies to mitigate these problems. To better mimic the ability of humans to learn from compact semantic description of tasks, we then propose a zero-shot learning strategy to recognize never-seen artworks, instead relying solely on textual descriptions of the target artworks. Then we look at the problem of learning from scarce data for the no-reference image quality assessment (NR-IQA) problem. IQA is an application for which data is notoriously scarce due to the elevated cost for annotation. Humans have an innate ability to inductively generalize from a limited number of examples, and to better mimic this we propose a generative model able to generate controlled perturbations of the input image, with the goal of synthetically increase the number of training instances used to train the network to estimate input image quality. Finally, we focus on the problem of catastrophic forgetting in recurrent neural networks, using image captioning as problem domain. We propose two strategies for defining continual image captioning experimental protocols and develop a continual learning framework for image captioning models based on encoder-decoder architectures. A task is defined by a set of object categories that appears in the images that we want the model to be able to describe. We observe that catastrophic forgetting is even more pronounced in this setting and establish several baselines by adapting existing state-of-the-art techniques to our continual image captioning problem. Then, to mimic the human ability to retain and leverage past knowledge when acquiring new tasks, we propose to use a mask-based technique that allocates specific neurons to each task only during backpropagation. This way, novel tasks do not interfere with the previous ones and forgetting is avoided. At the same time, past knowledge is exploited thanks to the ability of the network to use neurons allocated to previous tasks during the forward pass, which in turn reduces the number of neurons needed to learn each new task.
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YAYA, GULARA. "Impact of smartphone photography on memory: visual recognition memory after exposure to direct image and mediated image of artworks." Doctoral thesis, 2021. http://hdl.handle.net/11573/1568760.

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Unprecedented access and frequent use of smartphone cameras is not only reconstructing the way we communicate and share, but also the way we remember. Previous work has shown that photographing a scene can have detrimental effects on memory. In a set of experiments, we investigated whether the act of taking photographs with a smartphone led to poorer memory. Participants were presented a mock museum tour. Differently from real life museum tours, this made it possible to control for confounding variables potentially undetected in previous research. Participants were directed to merely observe the artworks or to photograph them depending on the group they were assigned to. In the first two experiments we manipulated encoding condition. In Experiment 1, intentional encoding took place such that participants were informed before the tour of a later memory test. The procedure was identical in Experiment 2, except that that time participants underwent a surprise recognition test. The results of Experiment 1 revealed that taking many photos impaired participants’ accuracy in remembering, whereas this impairment effect was eliminated in Experiment 2. This suggests that knowing in advance about a memory task creates itself the impairment by possibly affecting the retrieval strategies. Furthermore, photo groups in both experiments gave lower confidence ratings compared to no-photo groups, suggesting that photo taking makes people uncertain about what they remember. In Experiment 3, we aimed to replicate the photo-taking-impairment effect while testing for the effect of encoding by presenting for half of the retrieval cues only partial details of the original paintings. Overall, presenting only details impaired memory, and more importantly, the impairment effect was confirmed. However, no interaction was found with type of cues. Considered together, these results suggest that taking many photos does not impair encoding, while it seems to affect metacognitive variables at retrieval, such as confidence in memory and retrieval strategies. Further studies will examine this possibility directly.
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Books on the topic "Artwork Recognition"

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Gover, K. E. Art and Authority. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198768692.001.0001.

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Art and Authority is a philosophical essay on artistic authority and freedom: its sources, nature, and limits. It draws upon real-world cases and controversies in contemporary visual art and connects them to significant theories in the philosophical literature on art and aesthetics. Artworks, it is widely agreed, are the products of intentional human activity. And yet they are different from other kinds of artifacts; for one thing, they are meaningful. It is often presumed that artworks are an extension of their makers’ personality in ways that other kinds of artifacts are not. This is clear from our recognition that an artist continues to own his or her creation even once the art object, in which the artwork inheres, belongs to another. But it is far from clear how or why artists acquire this authority, and whether it originates from a special, intimate bond between artist and artwork. In response to these questions, the book argues for a ‘dual-intention theory’ of artistic authorship, in which it is claimed that authorship entails two orders of intention. The first, ‘generative’ moment, names the intentions that lead to the production of an artwork. The second, ‘evaluative’ moment, names the decision in which the artist decides whether or not to accept the artwork as part of their corpus.
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Nishime, Leilani. Seeing Multiracial. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038075.003.0007.

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This chapter uses the artwork of Kip Fulbeck as a lever to pry open some of the thornier matters surrounding the twinned issues of recognition and state-sponsored discipline. Fulbeck's most famous work, The Hapa Project, is included in the traveling anthropological exhibit “Race: Are We So Different?” The chapter puts Fulbeck's artwork in dialogue with the history of race-based scientific photography and argues that the exhibit's representation of multiracial Asian Americans can provide a counternarrative to the re-racialization of genetic science. Thus, Fulbeck's work demonstrates how audiences might view multiracial visual culture less as an antidote to racial hierarchies and more as a tool that can break open the smooth surface of naturalized and transcendent notions of racial difference.
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Baker, Courtney R., ed. Framed and Shamed. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252039485.003.0003.

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This chapter examines the recoding of images of lynchings that transformed the look from one of private pleasure to one of public disgust. It highlights an example of this counter-look, or look that endeavors to undo and even vilify the initial approving looks that lynching images invited: the look of shame that operates as a kind of social policing mechanism, one that diminishes the possibility for the consumption of lynching imagery as pleasurable and entertaining. The chapter compares a recent exhibition of lynching photography with a mid-century exhibition of antilynching artwork, suggesting that different evaluative criteria—aesthetic, ideological, realist, documentary—imply different political interpretations of lynching imagery. By analyzing the aesthetics of lynching, the chapter shows how a particular kind of looking is privileged and compels the recognition of certain bodies as human. Lynching, as an event, makes obvious the presence and potency of white humanity as it obliterated the possibility of black humanity.
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Halmi, Nicholas. The Greco-Roman Revival. Edited by David Duff. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199660896.013.42.

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During the eighteenth century an emergent historicism, which differentiated modernity radically from past ages, questioned the traditional notion of a ‘classical tradition’ of timeless values exemplified in Greek and Roman works. Classical antiquity began to be understood as a repository of historical artefacts associated, in part nostalgically, with ‘primitive’ ways of thought. Such recognition of the distance between modernity and antiquity paradoxically encouraged identification with the latter, since antiquarian research permitted increasingly accurate imitation of classical forms in the visual arts from the 1750s, while anthropological reflection on myth stimulated a revival of mythological poetry from the 1810s. Yet British Romantic poetry, whether describing classical artworks or appropriating classical myths, engaged with classical antiquity ambivalently, often ironically. While espousing the Philhellenist cause of Greek independence from Ottoman rule, Byron and Shelley remained very conscious of the disparities between ancient and modern Greece.
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Book chapters on the topic "Artwork Recognition"

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Chiaro, Riccardo Del, Andrew D. Bagdanov, and Alberto Del Bimbo. "NoisyArt: Exploiting the Noisy Web for Zero-shot Classification and Artwork Instance Recognition." In Data Analytics for Cultural Heritage, 1–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66777-1_1.

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Cetinic, Eva. "Iconographic Image Captioning for Artworks." In Pattern Recognition. ICPR International Workshops and Challenges, 502–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68796-0_36.

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Becattini, Federico, Andrea Ferracani, Lea Landucci, Daniele Pezzatini, Tiberio Uricchio, and Alberto Del Bimbo. "Imaging Novecento. A Mobile App for Automatic Recognition of Artworks and Transfer of Artistic Styles." In Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection, 781–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48496-9_62.

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Seeley, William P. "Fixing Testadura’s Error." In Attentional Engines, 219–29. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190662158.003.0009.

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Chapter 8 returns to the puzzle of locating art. It explores a range of case studies which demonstrate how categorizing an artwork guides attention and shapes perception. The discussion thereby demonstrates that knowledge of normative conventions governing artistic appreciation shapes the perception of artworks. The psychology and neuroscience of perception can be used to model and explain these processes. The diagnostic recognition framework for engaging art therefore obviates the common perceptual mechanisms, dissolves the normative dimension of appreciation arguments, and points toward a resolution of the puzzle of locating art.
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Brown, Steven. "Emotion and the arts." In The Unification of the Arts, 73–118. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198864875.003.0003.

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While many historical approaches equate the arts with aesthetics, I see the arts far more broadly than that. In addition, aesthetic emotions themselves need to be grounded in a general theory of human emotion. Along these lines, this chapter presents a communication model of emotion that covers the full gamut of processes from the production to the perception of emotion in the arts. The production side includes compositional processes for imbuing an artwork with emotions, as well as performance processes for conveying the emotions contained in an artwork. The perceptual mechanisms include recognition of the emotional content of an artwork, as well as the experience of felt emotions by people in response to such a work. The latter is where aesthetic emotions are situated in the model.
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Thomson-Jones, Katherine. "Digital Ontology and Appreciation." In Image in the Making, 48–61. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197567616.003.0003.

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This chapter takes up the aesthetic implications of images belonging to digital schemes. I consider how the most basic feature of digital scheme types—namely, their inherent replicability—properly conditions appreciation of digital artworks. Returning to the art examples from chapter 1, I show that the inherent replicability of digital imagery does not guarantee the multiple instantiability of digital image-based works. In the digital age artists face a choice as to whether to limit their works to a particular exhibition space, or make their works universally available on the screens of networked computers. I explore the interpretive significance of this choice and the way such significance rests on recognition of the underlying digital structure of an artwork.
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Ravetto-Biagioli, Kriss. "Uncanny Feedback." In Digital Uncanny, 97–142. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190853990.003.0004.

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Combining dance with digital and sound art, the performances of Simon Biggs, Sue Hawksley, and Garth Paine produce uncanny effects at the interface of the movement of the performer’s body with the software and technological apparatus that track, map, and interact with it. In Bodytext, Crosstalk, Blowup and Dark Matter the uncanny is neither presented as a questioning of subjectivity nor does it emerge from the viewer’s experience of the artwork, but is rather created within the artwork itself, from the feedback among its different interacting elements. “Uncanny Feedback” examines how interactivity between humans and digital media generates uncanny events. Interactivity is not simply a play of surface effects but a complex interactive performance that explores the inter-relationships between kinesthetic experiences and memory, muscle memory and intentional movement, and dance as an imagined movement, a form of interaction, gesture and response to voice recognition, sonification and audio programming.
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Seeley, William P. "Seeking Salience." In Attentional Engines, 57–92. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190662158.003.0003.

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Chapter 2 explores the role played by categorization processing in perceptual recognition and introduces a diagnostic recognition framework for engaging art derived from a biased competition model for selective attention. The environment is replete with information. Perceptual systems are limited capacity cognitive systems. Perceptual systems are by their very nature, therefore, selective. In ordinary contexts, task demands and general world knowledge are used to direct attention to task-salient features of the environment. In artistic contexts these task demands are constrained by shared knowledge of different categories of art which serve as recipes to direct attention to minimal sets of diagnostic compositional features that carry the content of a work. Neurophysiological evidence demonstrates that these psychological processes not only guide attention, but also shape perception. This in turn entails that psychology and neuroscience can contribute to an understanding of how an artwork carries and conveys its artistically salient content.
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So, Anthony Man-Cho. "Technical Elements of Machine Learning for Intellectual Property Law." In Artificial Intelligence and Intellectual Property, 11–27. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198870944.003.0002.

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Recent advances in artificial intelligence (AI) technologies have transformed our lives in profound ways. Indeed, AI has not only enabled machines to see (eg, face recognition), hear (eg, music retrieval), speak (eg, speech synthesis), and read (eg, text processing), but also, so it seems, given machines the ability to think (eg, board game-playing) and create (eg, artwork generation). This chapter introduces the key technical elements of machine learning (ML), which is a rapidly growing sub-field in AI and drives many of the aforementioned applications. The goal is to elucidate the ways human efforts are involved in the development of ML solutions, so as to facilitate legal discussions on intellectual property issues.
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Mitcheson, Katrina. "Conclusion." In Visual Art and Self-Construction, 141–45. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9780748693672.003.0007.

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The conclusion summarises the thesis of the book, which argues that narrative theories of the self are too narrow in their understanding of self-construction, neglecting the body and simplifying agency, and sets out an alternative corporeal hermeneutics of the self that draws on visual art. The conclusion also revisits the circularity that Ricoeur recognises between self and culture and starts to explore how in Joseph Beuys notion of Social Sculpture we go further than this recognition that the worlds of the artwork and agency influence and depend on each other; seeing social action as itself a form of artistic agency. Given our intersubjectivity and our dependence on our environment, engaging in practices which change the world around us is continuous with our hermeneutics of the self. New ways of relating to our identity can also involve new ways of relating to others, and to the environment, both cultural and natural; such as Beuys’ attempted with his 7,000 Oaks (1982–6).
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Conference papers on the topic "Artwork Recognition"

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Del Chiaro, R., A. Bagdanov, and A. Del Bimbo. "NoisyArt: A Dataset for Webly-supervised Artwork Recognition." In 14th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007392704670475.

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Jin, Xun, and Jongweon Kim. "ArtWork recognition in 360-degree image using 32-hedron based rectilinear projection and scale invariant feature transform." In 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2016. http://dx.doi.org/10.1109/iceict.2016.7879716.

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Hosain, Md Kamran, Harun-Ur-Rashid, Tasnova Bintee Taher, and Mohammad Masudur Rahman. "Genre Recognition of Artworks using Convolutional Neural Network." In 2020 23rd International Conference on Computer and Information Technology (ICCIT). IEEE, 2020. http://dx.doi.org/10.1109/iccit51783.2020.9392688.

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Gao, Jipeng, Haolin Zhou, and Yicheng Zhang. "The Performance of Two CNN Methods in Artworks Aesthetic Feature Recognition." In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383972.3383974.

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Tomei, Matteo, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara. "Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-To-Image Translation." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00600.

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You might also be interested in the extended bibliographies on the topic 'Artwork Recognition' for particular source types:
Journal articles
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