Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Deep learning-based graffiti detection: A study using Images from the streets of Lisbon

This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set.info:eu-repo/semantics/publishedVersio

Graffiti Museum: A First Amendment Argument for Protecting Uncommissioned art on Private Property

Graffiti has long been a target of municipal legislation that aims to preserve property values, public safety, and aesthetic integrity in the community. Not only are graffitists at risk of criminal prosecution but property owners are subject to civil and criminal penalties for harboring graffiti on their land. Since the 1990s, most U.S. cities have promulgated graffiti abatement ordinances that require private property owners to remove graffiti from their land, often at their own expense. These ordinances define graffiti broadly to include essentially any surface marking applied without advance authorization from the property owner. Meanwhile, graffiti has risen in prominence as a legitimate art form, beginning in the 1960s and most recently with the contributions of street artists such as Banksy and Shepard Fairey. Some property owners may find themselves fortuitous recipients of graffiti they deem art and want to preserve in spite of graffiti abatement ordinances and sign regulations requiring the work\u27s removal. This Note argues that private property owners who wish to preserve uncommissioned art on their land can challenge these laws under the First Amendment, claiming that, as applied, regulations requiring removal are unconstitutional because they leave the property owner insufficient alternative channels for expression

Large-scale image collection cleansing, summarization and exploration

A perennially interesting topic in the research field of large scale image collection organization is how to effectively and efficiently conduct the tasks of image cleansing, summarization and exploration. The primary objective of such an image organization system is to enhance user exploration experience with redundancy removal and summarization operations on large-scale image collection. An ideal system is to discover and utilize the visual correlation among the images, to reduce the redundancy in large-scale image collection, to organize and visualize the structure of large-scale image collection, and to facilitate exploration and knowledge discovery. In this dissertation, a novel system is developed for exploiting and navigating large-scale image collection. Our system consists of the following key components: (a) junk image filtering by incorporating bilingual search results; (b) near duplicate image detection by using a coarse-to-fine framework; (c) concept network generation and visualization; (d) image collection summarization via dictionary learning for sparse representation; and (e) a multimedia practice of graffiti image retrieval and exploration. For junk image filtering, bilingual image search results, which are adopted for the same keyword-based query, are integrated to automatically identify the clusters for the junk images and the clusters for the relevant images. Within relevant image clusters, the results are further refined by removing the duplications under a coarse-to-fine structure. The duplicate pairs are detected with both global feature (partition based color histogram) and local feature (CPAM and SIFT Bag-of-Word model). The duplications are detected and removed from the data collection to facilitate further exploration and visual correlation analysis. After junk image filtering and duplication removal, the visual concepts are further organized and visualized by the proposed concept network. An automatic algorithm is developed to generate such visual concept network which characterizes the visual correlation between image concept pairs. Multiple kernels are combined and a kernel canonical correlation analysis algorithm is used to characterize the diverse visual similarity contexts between the image concepts. The FishEye visualization technique is implemented to facilitate the navigation of image concepts through our image concept network. To better assist the exploration of large scale data collection, we design an efficient summarization algorithm to extract representative examplars. For this collection summarization task, a sparse dictionary (a small set of the most representative images) is learned to represent all the images in the given set, e.g., such sparse dictionary is treated as the summary for the given image set. The simulated annealing algorithm is adopted to learn such sparse dictionary (image summary) by minimizing an explicit optimization function. In order to handle large scale image collection, we have evaluated both the accuracy performance of the proposed algorithms and their computation efficiency. For each of the above tasks, we have conducted experiments on multiple public available image collections, such as ImageNet, NUS-WIDE, LabelMe, etc. We have observed very promising results compared to existing frameworks. The computation performance is also satisfiable for large-scale image collection applications. The original intention to design such a large-scale image collection exploration and organization system is to better service the tasks of information retrieval and knowledge discovery. For this purpose, we utilize the proposed system to a graffiti retrieval and exploration application and receive positive feedback

Roxie, Mr. Bingo, Kewl and The Gate : Street Gangs in Kinston: Participating in One City's Game of Chance to Save Itself

This project in pursuit of social and aesthetic experience involved the Kinston Community Council for the Arts, the Kinston Department of Public Safety and The Gate of Kinston (Gang Awareness Training and Education; a community center for the development of youth). Following the Gate's mission to increase advocacy among police officers and youth, this project comprised art sessions, two days a week, one and a half hours each day over the course of three months. Participants included Jasmine Coleman, Caleb Fisher, Tavon Green, Officer Kevin Jenkins, Tayler Morgan, Alexis Monshay Sutton and Sergeant Dennis Taylor. Youth and police worked together on screenprinted images which were publicly exhibited in February 2010 at Kinston Community Council for the Arts. The core group of five students and two officers investigated ideas of perception, projection and what makes someone "cool". All were offered opportunity to socially act in taking control of their images and vocationally act by studying the rudimental tools of visual art and developing the entrepreneurial skill of screenprinting (addressing the belief that part of the problem Kinston faces with gang affiliation is economically driven). Socially, this applied process provided an atypical and productive environment encouraging officers and students to speak and listen outside their typical street interactions. With much instability present in the daily lives of at-risk youth, screenprinting offers youth a tangible form of communication and control; a marketable skill executed with one's own hands. The objective was to ascertain whether process, product, or both could build social capital, affect social and/or socio-economic change. The narrative and place-making created together, the history and narrative of place and the transfer of entrepreneurial skills to the students, all carried weight in measuring this endeavor's investigation and pursuit of art as an agent of transformation.  M.F.A

Protest Art and Copyright Law: Weaponizing Intellectual Property against Systemic Inequality and Social Injustice

The death of George Floyd ignited a powerful modern-day Civil Rights movement that spread across the globe. While some protesters took to the streets to demand change, creators amplified the message of hope and unity through protest street art. Murals of police brutality victims like George Floyd, Breonna Taylor, and Ahmaud Arbery, among many others, appeared in most large cities in the United States and were widely spread on social media. From cave art to modern protest street art, graffiti continues to be a generational medium of expression of the human experience. However, while a handful of artists like Banksy, Keith Haring, and Basquiat are celebrated, lesser-known graffiti artists face prosecution and fines. The cognitive dissonance at the heart of this debate grows more evident as graffiti art is commercialized, while still considered an act of vandalism. Copyright law has long protected economic rights of artists, encompassing unauthorized reproduction and distribution of the works. Moral rights of artists against destruction and mutilation were ignored until the passage of Visual Artists Rights Act ( VARA ) in 1990. Today, 17 U.S.C. § 106A recognizes moral rights of attribution and integrity for a limited category of visual artworks of “recognized stature.” By failing to issue legislative guidance defining the recognized stature standard, Congress left this critical element to subjective judicial interpretation. The outcome creates a fundamentally flawed standard that ignores potential prejudice and dislike of graffiti as an art form. In Castillo v. G&M Realty LP, the United States Court of Appeals for the Second Circuit upheld a judgment against a New York developer for painting over graffiti, thereby violating artists\u27 rights under VARA. Although Castillo was a landmark case for graffiti artists, its holding further narrowed the scope of VARA. This Note discusses the potential negative effect of Castillo on future graffiti art cases litigated under VARA. It also proposes an amendment to VARA which will help limit judicial bias and ensure a fair and equitable application of the law for graffiti artists