Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete clientserver image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

Living with the Semantic Gap: Experiences and remedies in the context of medical imaging

Semantic annotation of images is a key concern for the newly emerged applications of semantic multimedia. Machine processable descriptions of images make it possible to automate a variety of tasks from search and discovery to composition and collage of image data bases. However, the ever occurring problem of the semantic gap between the low level descriptors and the high level interpretation of an image poses new challenges and needs to be addressed before the full potential of semantic multimedia can be realised. We explore the possibilities and lessons learnt with applied semantic multimedia from our engagement with medical imaging where we deployed ontologies and a novel distributed architecture to provide semantic annotation, decision support and methods for tackling the semantic gap problem

VFX – A New Frontier: The Impact of Innovative Technology on Visual Effects

Although Visual Effects (VFX) are an increasingly important element of the media content demanded by audiences, of media production (filmmaking and storytelling) and of the media industries, VFX remains a relatively under-research area within academic media or film studies. Innovations in technology are instrumental to the continuous developments in VFX technology, enabling the evolution of storytelling techniques and expanding the boundaries of VFX content and the VFX industries. In particular, a new wave of cutting-edge technologies have contributed to a period of extensive technical and organisational changes in the VFX industry. The implementation of these technologies is occurring during a period of growth in demand for VFX content, ever hight standards of quality (in particular the realism of VFX effects) and resulting demand for VFX workers. Supplying this demand for both greater quantity and quality of VFX content has increased the pressure for VFX production to be as efficient as possible. This has brought pressure on production budgets (to produce more and better content from the same or even diminishing resources) and production timeframes (“turnaround times”). One result of all these changes is that VFX workers now confront a multitude of new challenges. This study investigates the new technology which is driving or enabling these changes and in particular focuses on the impact of implementing these technologies on VFX production (the VFX workflow). The study collects evidence to show how these new technologies, combined with the broader changes in the industry, are impacting VFX production and labour. The thesis approaches this research task by use economic and sociological theories of technology, innovation, and production/labour to provide a conceptual framework to use in understanding how these changes are impacting the products produced by the industry and the work experience of VFX professionals. The next step is to fill in the gaps in knowledge resulting from the relatively under-researched nature of VFX production withing academic media and film studies. The thesis provides a detailed account of the emergence and growth of “the VFX industry”, including historical and current product and process innovations. Rather than defining the object of study in relation to content genres or types of business, the study defines the industry in terms of workers using a common set of tools. This section of the thesis explores the economic and cultural causes of changes in the industry and maps out the qualitative changes in the creativity, job satisfaction and job security/precarity of VFX labour. The collection of primary data through interviews with industry professionals provides the unique contribution of this study, setting out how VFX work is changing in different content genres, types of business and production roles, at different hierarchical levels. This study contributes to the field by addressing the need for academic and empirical research in this neglected area of study. The thesis contributes original knowledge on the impact of current technological innovations by providing research based on primary data collected from interviews with the VFX workers impacted by the implementation of the technologies. Potential policy and practical applications of this research include assisting industry professionals in deconstructing the marketing “hype” around these cutting-edge technologies and outlining uncertainties and implications of these technologies, helping them in the complex decision making of evaluating and implementing current innovative technology

Automated mood boards - Ontology-based semantic image retrieval

The main goal of this research is to support concept designers’ search for inspirational and meaningful images in developing mood boards. Finding the right images has become a well-known challenge as the amount of images stored and shared on the Internet and elsewhere keeps increasing steadily and rapidly. The development of image retrieval technologies, which collect, store and pre-process image information to return relevant images instantly in response to users’ needs, have achieved great progress in the last decade. However, the keyword-based content description and query processing techniques for Image Retrieval (IR) currently used have their limitations. Most of these techniques are adapted from the Information Retrieval research, and therefore provide limited capabilities to grasp and exploit conceptualisations due to their inability to handle ambiguity, synonymy, and semantic constraints. Conceptual search (i.e. searching by meaning rather than literal strings) aims to solve the limitations of the keyword-based models. Starting from this point, this thesis investigates the existing IR models, which are oriented to the exploitation of domain knowledge in support of semantic search capabilities, with a focus on the use of lexical ontologies to improve the semantic perspective. It introduces a technique for extracting semantic DNA (SDNA) from textual image annotations and constructing semantic image signatures. The semantic signatures are called semantic chromosomes; they contain semantic information related to the images. Central to the method of constructing semantic signatures is the concept disambiguation technique developed, which identifies the most relevant SDNA by measuring the semantic importance of each word/phrase in the image annotation. In addition, a conceptual model of an ontology-based system for generating visual mood boards is proposed. The proposed model, which is adapted from the Vector Space Model, exploits the use of semantic chromosomes in semantic indexing and assessing the semantic similarity of images within a collection

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As othe