An integrative semantic framework for image annotation and retrieval

Most public image retrieval engines utilise free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. Our semantic retrieval technology is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. We also present our efforts in further improving the recall of our retrieval technology by deploying an efficient query expansion technique

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services

Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers

TOWARDS AN INTEGRATIVE THEORETICAL FRAMEWORK OF INTERACTIVE MACHINE LEARNING SYSTEMS

Interactive machine learning (IML) is a learning process in which a user interacts with a system to iteratively define and optimise a model. Although recent years have illustrated the proliferation of IML systems in the fields of Human-Computer Interaction (HCI), Information Systems (IS), and Computer Science (CS), current research results are scattered leading to a lack of integration of existing work on IML. Furthermore, due to diverging functionalities and purposes IML systems can refer to, an uncertainty exists regarding the underlying distinct capabilities that constitute this class of systems. By reviewing extensive IML literature, this paper suggests an integrative theoretical framework for IML systems to address these current impediments. Reviewing 2,879 studies in leading journals and conferences during the years 1966-2018, we found an extensive range of applications areas that have implemented IML systems and the necessity to standardise the evaluation of those systems. Our framework offers an essential step to provide a theoretical foundation to integrate concepts and findings across different fields of research. The main contribution of this paper is organising and structuring the body of knowledge in IML for the advancement of the field. Furthermore, we suggest three opportunities for future IML research. From a practical point of view, our integrative theoretical framework can serve as a reference guide to inform the design and implementation of IML systems

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability

Video Storytelling: Textual Summaries for Events

Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this work, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a Residual Bidirectional Recurrent Neural Network to leverage contextual information from past and future. Second, we propose a Narrator model to discover the underlying storyline. The Narrator is formulated as a reinforcement learning agent which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the Video Story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines, and show that our method achieves better performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research