Recuperação multimodal e interativa de informação orientada por diversidade

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os métodos de Recuperação da Informação, especialmente considerando-se dados multimídia, evoluíram para a integração de múltiplas fontes de evidência na análise de relevância de itens em uma tarefa de busca. Neste contexto, para atenuar a distância semântica entre as propriedades de baixo nível extraídas do conteúdo dos objetos digitais e os conceitos semânticos de alto nível (objetos, categorias, etc.) e tornar estes sistemas adaptativos às diferentes necessidades dos usuários, modelos interativos que consideram o usuário mais próximo do processo de recuperação têm sido propostos, permitindo a sua interação com o sistema, principalmente por meio da realimentação de relevância implícita ou explícita. Analogamente, a promoção de diversidade surgiu como uma alternativa para lidar com consultas ambíguas ou incompletas. Adicionalmente, muitos trabalhos têm tratado a ideia de minimização do esforço requerido do usuário em fornecer julgamentos de relevância, à medida que mantém níveis aceitáveis de eficácia. Esta tese aborda, propõe e analisa experimentalmente métodos de recuperação da informação interativos e multimodais orientados por diversidade. Este trabalho aborda de forma abrangente a literatura acerca da recuperação interativa da informação e discute sobre os avanços recentes, os grandes desafios de pesquisa e oportunidades promissoras de trabalho. Nós propusemos e avaliamos dois métodos de aprimoramento do balanço entre relevância e diversidade, os quais integram múltiplas informações de imagens, tais como: propriedades visuais, metadados textuais, informação geográfica e descritores de credibilidade dos usuários. Por sua vez, como integração de técnicas de recuperação interativa e de promoção de diversidade, visando maximizar a cobertura de múltiplas interpretações/aspectos de busca e acelerar a transferência de informação entre o usuário e o sistema, nós propusemos e avaliamos um método multimodal de aprendizado para ranqueamento utilizando realimentação de relevância sobre resultados diversificados. Nossa análise experimental mostra que o uso conjunto de múltiplas fontes de informação teve impacto positivo nos algoritmos de balanceamento entre relevância e diversidade. Estes resultados sugerem que a integração de filtragem e re-ranqueamento multimodais é eficaz para o aumento da relevância dos resultados e também como mecanismo de potencialização dos métodos de diversificação. Além disso, com uma análise experimental minuciosa, nós investigamos várias questões de pesquisa relacionadas à possibilidade de aumento da diversidade dos resultados e a manutenção ou até mesmo melhoria da sua relevância em sessões interativas. Adicionalmente, nós analisamos como o esforço em diversificar afeta os resultados gerais de uma sessão de busca e como diferentes abordagens de diversificação se comportam para diferentes modalidades de dados. Analisando a eficácia geral e também em cada iteração de realimentação de relevância, nós mostramos que introduzir diversidade nos resultados pode prejudicar resultados iniciais, enquanto que aumenta significativamente a eficácia geral em uma sessão de busca, considerando-se não apenas a relevância e diversidade geral, mas também o quão cedo o usuário é exposto ao mesmo montante de itens relevantes e nível de diversidadeAbstract: Information retrieval methods, especially considering multimedia data, have evolved towards the integration of multiple sources of evidence in the analysis of the relevance of items considering a given user search task. In this context, for attenuating the semantic gap between low-level features extracted from the content of the digital objects and high-level semantic concepts (objects, categories, etc.) and making the systems adaptive to different user needs, interactive models have brought the user closer to the retrieval loop allowing user-system interaction mainly through implicit or explicit relevance feedback. Analogously, diversity promotion has emerged as an alternative for tackling ambiguous or underspecified queries. Additionally, several works have addressed the issue of minimizing the required user effort on providing relevance assessments while keeping an acceptable overall effectiveness. This thesis discusses, proposes, and experimentally analyzes multimodal and interactive diversity-oriented information retrieval methods. This work, comprehensively covers the interactive information retrieval literature and also discusses about recent advances, the great research challenges, and promising research opportunities. We have proposed and evaluated two relevance-diversity trade-off enhancement work-flows, which integrate multiple information from images, such as: visual features, textual metadata, geographic information, and user credibility descriptors. In turn, as an integration of interactive retrieval and diversity promotion techniques, for maximizing the coverage of multiple query interpretations/aspects and speeding up the information transfer between the user and the system, we have proposed and evaluated a multimodal learning-to-rank method trained with relevance feedback over diversified results. Our experimental analysis shows that the joint usage of multiple information sources positively impacted the relevance-diversity balancing algorithms. Our results also suggest that the integration of multimodal-relevance-based filtering and reranking was effective on improving result relevance and also boosted diversity promotion methods. Beyond it, with a thorough experimental analysis we have investigated several research questions related to the possibility of improving result diversity and keeping or even improving relevance in interactive search sessions. Moreover, we analyze how much the diversification effort affects overall search session results and how different diversification approaches behave for the different data modalities. By analyzing the overall and per feedback iteration effectiveness, we show that introducing diversity may harm initial results whereas it significantly enhances the overall session effectiveness not only considering the relevance and diversity, but also how early the user is exposed to the same amount of relevant items and diversityDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoP-4388/2010140977/2012-0CAPESCNP

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

Mining photographic collections to enhance the precision and recall of search results using semantically controlled query expansion

Driven by a larger and more diverse user-base and datasets, modern Information Retrieval techniques are striving to become contextually-aware in order to provide users with a more satisfactory search experience. While text-only retrieval methods are significantly more accurate and faster to render results than purely visual retrieval methods, these latter provide a rich complementary medium which can be used to obtain relevant and different results from those obtained using text-only retrieval. Moreover, the visual retrieval methods can be used to learn the user’s context and preferences, in particular the user’s relevance feedback, and exploit them to narrow down the search to more accurate results. Despite the overall deficiency in precision of visual retrieval result, the top results are accurate enough to be used for query expansion, when expanded in a controlled manner. The method we propose overcomes the usual pitfalls of visual retrieval: 1. The hardware barrier giving rise to prohibitively slow systems. 2. Results dominated by noise. 3. A significant gap between the low-level features and the semantics of the query. In our thesis, the first barrier is overcome by employing a simple block-based visual features which outperforms a method based on MPEG-7 features specially at early precision (precision of the top results). For the second obstacle, lists from words semantically weighted according to their degree of relation to the original query or to relevance feedback from example images are formed. These lists provide filters through which the confidence in the candidate results is assessed for inclusion in the results. This allows for more reliable Pseudo-Relevance Feedback (PRF). This technique is then used to bridge the third barrier; the semantic gap. It consists of a second step query, re-querying the data set with an query expanded with weighted words obtained from the initial query, and semantically filtered (SF) without human intervention. We developed our PRF-SF method on the IAPR TC-12 benchmark dataset of 20,000 tourist images, obtaining promising results, and tested it on the different and much larger Belga benchmark dataset of approximately 500,000 news images originating from a different source. Our experiments confirmed the potential of the method in improving the overall Mean Average Precision, recall, as well as the level of diversity of the results measured using cluster recall

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

Um arcabouço multimodal para geocodificação de objetos digitais

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Informação geográfica é usualmente encontrada em objetos digitais (como documentos, imagens e vídeos), sendo de grande interesse utilizá-la na implementação de diferentes serviços. Por exemplo, serviços de navegação baseados em mapas e buscas geográficas podem se beneficiar das localizações geográficas associadas a objetos digitais. A implementação destes serviços, no entanto, demanda o uso de coleções de dados geocodificados. Este trabalho estuda a combinação de conteúdo textual e visual para geocodificar objetos digitais e propõe um arcabouço de agregação de listas para geocodificação multimodal. A informação textual e visual de vídeos e imagens é usada para definir listas ordenadas. Em seguida, elas são combinadas e a nova lista ordenada resultante é usada para definir a localização geográfica de vídeos e imagens. Uma arquitetura que implementa essa proposta foi projetada de modo que módulos específicos para cada modalidade (e.g., textual ou visual) possam ser aperfeiçoados independentemente. Outro componente é o módulo de fusão responsável pela combinação das listas ordenadas definidas por cada modalidade. Outra contribuição deste trabalho é a proposta de uma nova medida de avaliação da efetividade de métodos de geocodificação chamada Weighted Average Score (WAS). Ela é baseada em ponderações de distâncias que permitem avaliar a efetividade de uma abordagem, considerando todos os resultados de geocodificação das amostras de teste. O arcabouço proposto foi validado em dois contextos: desafio Placing Task da iniciativa MediaEval 2012, que consiste em atribuir, automaticamente, coordenadas geográficas a vídeos; e geocodificação de fotos de prédios da Virginia Tech (VT) nos EUA. No contexto do desafio Placing Task, os resultados mostram como nossa abordagem melhora a geocodificação em comparação a métodos que apenas contam com uma modalidade (sejam descritores textuais ou visuais). Nós mostramos ainda que a proposta multimodal produziu resultados comparáveis às melhores submissões que também não usavam informações adicionais além daquelas disponibilizadas na base de treinamento. Em relação à geocodificação das fotos de prédios da VT, os experimentos demostraram que alguns dos descritores visuais locais produziram resultados efetivos. A seleção desses descritores e sua combinação melhoraram esses resultados quando a base de conhecimento tinha as mesmas características da base de testeAbstract: Geographical information is often enclosed in digital objects (like documents, images, and videos) and its use to support the implementation of different services is of great interest. For example, the implementation of map-based browser services and geographic searches may take advantage of geographic locations associated with digital objects. The implementation of such services, however, demands the use of geocoded data collections. This work investigates the combination of textual and visual content to geocode digital objects and proposes a rank aggregation framework for multimodal geocoding. Textual and visual information associated with videos and images are used to define ranked lists. These lists are later combined, and the new resulting ranked list is used to define appropriate locations. An architecture that implements the proposed framework is designed in such a way that specific modules for each modality (e.g., textual and visual) can be developed and evolved independently. Another component is a data fusion module responsible for combining seamlessly the ranked lists defined for each modality. Another contribution of this work is related to the proposal of a new effectiveness evaluation measure named Weighted Average Score (WAS). The proposed measure is based on distance scores that are combined to assess how effective a designed/tested approach is, considering its overall geocoding results for a given test dataset. We validate the proposed framework in two contexts: the MediaEval 2012 Placing Task, whose objective is to automatically assign geographical coordinates to videos; and the task of geocoding photos of buildings from Virginia Tech (VT), USA. In the context of Placing Task, obtained results show how our multimodal approach improves the geocoding results when compared to methods that rely on a single modality (either textual or visual descriptors). We also show that the proposed multimodal approach yields comparable results to the best submissions to the Placing Task in 2012 using no additional information besides the available development/training data. In the context of the task of geocoding VT building photos, performed experiments demonstrate that some of the evaluated local descriptors yield effective results. The descriptor selection criteria and their combination improved the results when the used knowledge base has the same characteristics of the test setDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Medical Image Retrieval Using Multimodal Semantic Indexing

Large collections of medical images have become a valuable source of knowledge, taking an important role in education, medical research and clinical decision making. An important unsolved issue that is actively investigated is the efficient and effective access to these repositories. This work addresses the problem of information retrieval in large collections of biomedical images, allowing to use sample images as alternative queries to the classic keywords. The proposed approach takes advantage of both modalities: text and visual information. The main drawback of the multimodal strategies is that the associated algorithms are memory and computation intensive. So, an important challenge addressed in this work is the design of scalable strategies, that can be applied efficiently and effectively in large medical image collections. The experimental evaluation shows that the proposed multimodal strategies are useful to improve the image retrieval performance, and are fully applicable to large image repositories.Maestrí

GEO-REFERENCED VIDEO RETRIEVAL: TEXT ANNOTATION AND SIMILARITY SEARCH

Ph.DDOCTOR OF PHILOSOPH

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems