Large Scale Retrieval and Generation of Image Descriptions

What is the story of an image? What is the relationship between pictures, language, and information we can extract using state of the art computational recognition systems? In an attempt to address both of these questions, we explore methods for retrieving and generating natural language descriptions for images. Ideally, we would like our generated textual descriptions (captions) to both sound like a person wrote them, and also remain true to the image content. To do this we develop data-driven approaches for image description generation, using retrieval-based techniques to gather either: (a) whole captions associated with a visually similar image, or (b) relevant bits of text (phrases) from a large collection of image + description pairs. In the case of (b), we develop optimization algorithms to merge the retrieved phrases into valid natural language sentences. The end result is two simple, but effective, methods for harnessing the power of big data to produce image captions that are altogether more general, relevant, and human-like than previous attempts

Developing the ArchAIDE Application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Pottery is of fundamental importance for understanding archaeological contexts, facilitating the understanding of production, trade flows, and social interactions. Pottery characterisation and the classification of ceramics is still a manual process, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. Specifically, ArchAIDE worked to support classification and interpretation work (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This article summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT (Information and Communications Technology) domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. ArchAIDE integrated a variety of novel and best-practice approaches, both in the creation of the app, and the communication of the project to a range of stakeholders

Challenges and opportunities of context-aware information access

Ubiquitous computing environments embedding a wide range of pervasive computing technologies provide a challenging and exciting new domain for information access. Individuals working in these environments are increasingly permanently connected to rich information resources. An appealing opportunity of these environments is the potential to deliver useful information to individuals either from their previous information experiences or external sources. This information should enrich their life experiences or make them more effective in their endeavours. Information access in ubiquitous computing environments can be made "context-aware" by exploiting the wide range context data available describing the environment, the searcher and the information itself. Realizing such a vision of reliable, timely and appropriate identification and delivery of information in this way poses numerous challenges. A central theme in achieving context-aware information access is the combination of information retrieval with multiple dimensions of available context data. Potential context data sources, include the user's current task, inputs from environmental and biometric sensors, associated with the user's current context, previous contexts, and document context, which can be exploited using a variety of technologies to create new and exciting possibilities for information access

Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation

The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world's landmark buildings and objects, Internet photo collections are now being used as a source for building such systems in a fully automatic fashion. This process typically consists of three steps: clustering large amounts of images by the objects they depict; determining object names from user-provided tags; and building a robust, compact, and efficient recognition index. To this date, however, there is little empirical information on how well current approaches for those steps perform in a large-scale open-set mining and recognition task. Furthermore, there is little empirical information on how recognition performance varies for different types of landmark objects and where there is still potential for improvement. With this paper, we intend to fill these gaps. Using a dataset of 500k images from Paris, we analyze each component of the landmark recognition pipeline in order to answer the following questions: How many and what kinds of objects can be discovered automatically? How can we best use the resulting image clusters to recognize the object in a query? How can the object be efficiently represented in memory for recognition? How reliably can semantic information be extracted? And finally: What are the limiting factors in the resulting pipeline from query to semantics? We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures

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

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

Unsupervised quantification of entity consistency between photos and text in real-world news

Das World Wide Web und die sozialen Medien übernehmen im heutigen Informationszeitalter eine wichtige Rolle für die Vermittlung von Nachrichten und Informationen. In der Regel werden verschiedene Modalitäten im Sinne der Informationskodierung wie beispielsweise Fotos und Text verwendet, um Nachrichten effektiver zu vermitteln oder Aufmerksamkeit zu erregen. Kommunikations- und Sprachwissenschaftler erforschen das komplexe Zusammenspiel zwischen Modalitäten seit Jahrzehnten und haben unter Anderem untersucht, wie durch die Kombination der Modalitäten zusätzliche Informationen oder eine neue Bedeutungsebene entstehen können. Die Anzahl gemeinsamer Konzepte oder Entitäten (beispielsweise Personen, Orte und Ereignisse) zwischen Fotos und Text stellen einen wichtigen Aspekt für die Bewertung der Gesamtaussage und Bedeutung eines multimodalen Artikels dar. Automatisierte Ansätze zur Quantifizierung von Bild-Text-Beziehungen können für zahlreiche Anwendungen eingesetzt werden. Sie ermöglichen beispielsweise eine effiziente Exploration von Nachrichten, erleichtern die semantische Suche von Multimedia-Inhalten in (Web)-Archiven oder unterstützen menschliche Analysten bei der Evaluierung der Glaubwürdigkeit von Nachrichten. Allerdings gibt es bislang nur wenige Ansätze, die sich mit der Quantifizierung von Beziehungen zwischen Fotos und Text beschäftigen. Diese Ansätze berücksichtigen jedoch nicht explizit die intermodalen Beziehungen von Entitäten, welche eine wichtige Rolle in Nachrichten darstellen, oder basieren auf überwachten multimodalen Deep-Learning-Techniken. Diese überwachten Lernverfahren können ausschließlich die intermodalen Beziehungen von Entitäten detektieren, die in annotierten Trainingsdaten enthalten sind. Um diese Forschungslücke zu schließen, wird in dieser Arbeit ein unüberwachter Ansatz zur Quantifizierung der intermodalen Konsistenz von Entitäten zwischen Fotos und Text in realen multimodalen Nachrichtenartikeln vorgestellt. Im ersten Teil dieser Arbeit werden neuartige Verfahren auf Basis von Deep Learning zur Extrahierung von Informationen aus Fotos vorgestellt, um Ereignisse (Events), Orte, Zeitangaben und Personen automatisch zu erkennen. Diese Verfahren bilden eine wichtige Voraussetzung, um die Beziehungen von Entitäten zwischen Bild und Text zu bewerten. Zunächst wird ein Ansatz zur Ereignisklassifizierung präsentiert, der neuartige Optimierungsfunktionen und Gewichtungsschemata nutzt um Ontologie-Informationen aus einer Wissensdatenbank in ein Deep-Learning-Verfahren zu integrieren. Das Training erfolgt anhand eines neu vorgestellten Datensatzes, der 570.540 Fotos und eine Ontologie mit 148 Ereignistypen enthält. Der Ansatz übertrifft die Ergebnisse von Referenzsystemen die keine strukturierten Ontologie-Informationen verwenden. Weiterhin wird ein DeepLearning-Ansatz zur Schätzung des Aufnahmeortes von Fotos vorgeschlagen, der Kontextinformationen über die Umgebung (Innen-, Stadt-, oder Naturaufnahme) und von Erdpartitionen unterschiedlicher Granularität verwendet. Die vorgeschlagene Lösung übertrifft die bisher besten Ergebnisse von aktuellen Forschungsarbeiten, obwohl diese deutlich mehr Fotos zum Training verwenden. Darüber hinaus stellen wir den ersten Datensatz zur Schätzung des Aufnahmejahres von Fotos vor, der mehr als eine Million Bilder aus den Jahren 1930 bis 1999 umfasst. Dieser Datensatz wird für das Training von zwei Deep-Learning-Ansätzen zur Schätzung des Aufnahmejahres verwendet, welche die Aufgabe als Klassifizierungs- und Regressionsproblem behandeln. Beide Ansätze erzielen sehr gute Ergebnisse und übertreffen Annotationen von menschlichen Probanden. Schließlich wird ein neuartiger Ansatz zur Identifizierung von Personen des öffentlichen Lebens und ihres gemeinsamen Auftretens in Nachrichtenfotos aus der digitalen Bibliothek Internet Archiv präsentiert. Der Ansatz ermöglicht es unstrukturierte Webdaten aus dem Internet Archiv mit Metadaten, beispielsweise zur semantischen Suche, zu erweitern. Experimentelle Ergebnisse haben die Effektivität des zugrundeliegenden Deep-Learning-Ansatzes zur Personenerkennung bestätigt. Im zweiten Teil dieser Arbeit wird ein unüberwachtes System zur Quantifizierung von BildText-Beziehungen in realen Nachrichten vorgestellt. Im Gegensatz zu bisherigen Verfahren liefert es automatisch neuartige Maße der intermodalen Konsistenz für verschiedene Entitätstypen (Personen, Orte und Ereignisse) sowie den Gesamtkontext. Das System ist nicht auf vordefinierte Datensätze angewiesen, und kann daher mit der Vielzahl und Diversität von Entitäten und Themen in Nachrichten umgehen. Zur Extrahierung von Entitäten aus dem Text werden geeignete Methoden der natürlichen Sprachverarbeitung eingesetzt. Examplarbilder für diese Entitäten werden automatisch aus dem Internet beschafft. Die vorgeschlagenen Methoden zur Informationsextraktion aus Fotos werden auf die Nachrichten- und heruntergeladenen Exemplarbilder angewendet, um die intermodale Konsistenz von Entitäten zu quantifizieren. Es werden zwei Aufgaben untersucht um die Qualität des vorgeschlagenen Ansatzes in realen Anwendungen zu bewerten. Experimentelle Ergebnisse für die Dokumentverifikation und die Beschaffung von Nachrichten mit geringer (potenzielle Fehlinformation) oder hoher multimodalen Konsistenz zeigen den Nutzen und das Potenzial des Ansatzes zur Unterstützung menschlicher Analysten bei der Untersuchung von Nachrichten.In today’s information age, the World Wide Web and social media are important sources for news and information. Different modalities (in the sense of information encoding) such as photos and text are typically used to communicate news more effectively or to attract attention. Communication scientists, linguists, and semioticians have studied the complex interplay between modalities for decades and investigated, e.g., how their combination can carry additional information or add a new level of meaning. The number of shared concepts or entities (e.g., persons, locations, and events) between photos and text is an important aspect to evaluate the overall message and meaning of an article. Computational models for the quantification of image-text relations can enable many applications. For example, they allow for more efficient exploration of news, facilitate semantic search and multimedia retrieval in large (web) archives, or assist human assessors in evaluating news for credibility. To date, only a few approaches have been suggested that quantify relations between photos and text. However, they either do not explicitly consider the cross-modal relations of entities – which are important in the news – or rely on supervised deep learning approaches that can only detect the cross-modal presence of entities covered in the labeled training data. To address this research gap, this thesis proposes an unsupervised approach that can quantify entity consistency between photos and text in multimodal real-world news articles. The first part of this thesis presents novel approaches based on deep learning for information extraction from photos to recognize events, locations, dates, and persons. These approaches are an important prerequisite to measure the cross-modal presence of entities in text and photos. First, an ontology-driven event classification approach that leverages new loss functions and weighting schemes is presented. It is trained on a novel dataset of 570,540 photos and an ontology with 148 event types. The proposed system outperforms approaches that do not use structured ontology information. Second, a novel deep learning approach for geolocation estimation is proposed that uses additional contextual information on the environmental setting (indoor, urban, natural) and from earth partitions of different granularity. The proposed solution outperforms state-of-the-art approaches, which are trained with significantly more photos. Third, we introduce the first large-scale dataset for date estimation with more than one million photos taken between 1930 and 1999, along with two deep learning approaches that treat date estimation as a classification and regression problem. Both approaches achieve very good results that are superior to human annotations. Finally, a novel approach is presented that identifies public persons and their co-occurrences in news photos extracted from the Internet Archive, which collects time-versioned snapshots of web pages that are rarely enriched with metadata relevant to multimedia retrieval. Experimental results confirm the effectiveness of the deep learning approach for person identification. The second part of this thesis introduces an unsupervised approach capable of quantifying image-text relations in real-world news. Unlike related work, the proposed solution automatically provides novel measures of cross-modal consistency for different entity types (persons, locations, and events) as well as the overall context. The approach does not rely on any predefined datasets to cope with the large amount and diversity of entities and topics covered in the news. State-of-the-art tools for natural language processing are applied to extract named entities from the text. Example photos for these entities are automatically crawled from the Web. The proposed methods for information extraction from photos are applied to both news images and example photos to quantify the cross-modal consistency of entities. Two tasks are introduced to assess the quality of the proposed approach in real-world applications. Experimental results for document verification and retrieval of news with either low (potential misinformation) or high cross-modal similarities demonstrate the feasibility of the approach and its potential to support human assessors to study news