Learning graphs to model visual objects across different depictive styles

Abstract. Visual object classification and detection are major prob-lems in contemporary computer vision. State-of-art algorithms allow t-housands of visual objects to be learned and recognized, under a wide range of variations including lighting changes, occlusion, point of view and different object instances. Only a small fraction of the literature ad-dresses the problem of variation in depictive styles (photographs, draw-ings, paintings etc.). This is a challenging gap but the ability to process images of all depictive styles and not just photographs has potential val-ue across many applications. In this paper we model visual classes using a graph with multiple labels on each node; weights on arcs and nodes indicate relative importance (salience) to the object description. Visual class models can be learned from examples from a database that contains photographs, drawings, paintings etc. Experiments show that our repre-sentation is able to improve upon Deformable Part Models for detection and Bag of Words models for classification

Detecting People in Artwork with CNNs

CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN's performance is the highest yet, it remains less than 60\% AP, suggesting further work is needed for the cross-depiction problem. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46604-0_57Comment: 14 pages, plus 3 pages of references; 7 figures in ECCV 2016 Workshop

Hierarchical Image Descriptions for Classification and Painting

The overall argument this thesis makes is that topological object structures captured within hierarchical image descriptions are invariant to depictive styles and offer a level of abstraction found in many modern abstract artworks. To show how object structures can be extracted from images, two hierarchical image descriptions are proposed. The first of these is inspired by perceptual organisation; whereas, the second is based on agglomerative clustering of image primitives. This thesis argues the benefits and drawbacks of each image description and empirically show why the second is more suitable in capturing object strucutures. The value of graph theory is demonstrated in extracting object structures, especially from the second type of image description. User interaction during the structure extraction process is also made possible via an image hierarchy editor. Two applications of object structures are studied in depth. On the computer vision side, the problem of object classification is investigated. In particular, this thesis shows that it is possible to classify objects regardless of their depictive styles. This classification problem is approached using a graph theoretic paradigm; by encoding object structures as feature vectors of fixed lengths, object classification can then be treated as a clustering problem in structural feature space and that actual clustering can be done using conventional machine learning techniques. The benefits of object structures in computer graphics are demonstrated from a Non-Photorealistic Rendering (NPR) point of view. In particular, it is shown that topological object structures deliver an appropriate degree of abstraction that often appears in well-known abstract artworks. Moreover, the value of shape simplification is demonstrated in the process of making abstract art. By integrating object structures and simple geometric shapes, it is shown that artworks produced in child-like paintings and from artists such as Wassily Kandinsky, Joan Miro and Henri Matisse can be synthesised and by doing so, the current gamut of NPR styles is extended. The whole process of making abstract art is built into a single piece of software with intuitive GUI.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design and Implementation of Online Learning Environments

This thesis describes a systematic approach in the design and implementation of online learning environments. This approach incorporates the principles of human learning as well as the best practices in software engineering. This thesis implements a conceptual model for the design, and it describes how software elements can be developed to comply with the model. In the context of this research two online environments are developed and analyzed. The end product of this approach is a robust and reusable software architecture, a framework for design, and an effective and engaging model suited to online learning environments

Design and Implementation of Online Learning Environments

This thesis describes a systematic approach in the design and implementation of online learning environments. This approach incorporates the principles of human learning as well as the best practices in software engineering. This thesis implements a conceptual model for the design, and it describes how software elements can be developed to comply with the model. In the context of this research two online environments are developed and analyzed. The end product of this approach is a robust and reusable software architecture, a framework for design, and an effective and engaging model suited to online learning environments

Design and Implementation of Online Learning Environments

This thesis describes a systematic approach in the design and implementation of online learning environments. This approach incorporates the principles of human learning as well as the best practices in software engineering. This thesis implements a conceptual model for the design, and it describes how software elements can be developed to comply with the model. In the context of this research two online environments are developed and analyzed. The end product of this approach is a robust and reusable software architecture, a framework for design, and an effective and engaging model suited to online learning environments