The uncertain representation ranking framework for concept-based video retrieval

Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance

Search beyond traditional probabilistic information retrieval

"This thesis focuses on search beyond probabilistic information retrieval. Three ap- proached are proposed beyond the traditional probabilistic modelling. First, term associ- ation is deeply examined. Term association considers the term dependency using a factor analysis based model, instead of treating each term independently. Latent factors, con- sidered the same as the hidden variables of ""eliteness"" introduced by Robertson et al. to gain understanding of the relation among term occurrences and relevance, are measured by the dependencies and occurrences of term sequences and subsequences. Second, an entity-based ranking approach is proposed in an entity system named ""EntityCube"" which has been released by Microsoft for public use. A summarization page is given to summarize the entity information over multiple documents such that the truly relevant entities can be highly possibly searched from multiple documents through integrating the local relevance contributed by proximity and the global enhancer by topic model. Third, multi-source fusion sets up a meta-search engine to combine the ""knowledge"" from different sources. Meta-features, distilled as high-level categories, are deployed to diversify the baselines. Three modified fusion methods are employed, which are re- ciprocal, CombMNZ and CombSUM with three expanded versions. Through extensive experiments on the standard large-scale TREC Genomics data sets, the TREC HARD data sets and the Microsoft EntityCube Web collections, the proposed extended models beyond probabilistic information retrieval show their effectiveness and superiority.

Language Models and Smoothing Methods for Information Retrieval

Language Models and Smoothing Methods for Information Retrieval (Sprachmodelle und Glättungsmethoden für Information Retrieval) Najeeb A. Abdulmutalib Kurzfassung der Dissertation Retrievalmodelle bilden die theoretische Grundlage für effektive Information-Retrieval-Methoden. Statistische Sprachmodelle stellen eine neue Art von Retrievalmodellen dar, die seit etwa zehn Jahren in der Forschung betrachtet werde. Im Unterschied zu anderen Modellen können sie leichter an spezifische Aufgabenstellungen angepasst werden und liefern häufig bessere Retrievalergebnisse. In dieser Dissertation wird zunächst ein neues statistisches Sprachmodell vorgestellt, das explizit Dokumentlängen berücksichtigt. Aufgrund der spärlichen Beobachtungsdaten spielen Glättungsmethoden bei Sprachmodellen eine wichtige Rolle. Auch hierfür stellen wir eine neue Methode namens 'exponentieller Glättung' vor. Der experimentelle Vergleich mit konkurrierenden Ansätzen zeigt, dass unsere neuen Methoden insbesondere bei Kollektionen mit stark variierenden Dokumentlängen überlegene Ergebnisse liefert. In einem zweiten Schritt erweitern wir unseren Ansatz auf XML-Retrieval, wo hierarchisch strukturierte Dokumente betrachtet werden und beim fokussierten Retrieval möglichst kleine Dokumentteile gefunden werden sollen, die die Anfrage vollständig beantworten. Auch hier demonstriert der experimentelle Vergleich mit anderen Ansätzen die Qualität unserer neu entwickelten Methoden. Der dritte Teil der Arbeit beschäftigt sich mit dem Vergleich von Sprachmodellen und der klassischen tf*idf-Gewichtung. Neben einem besseren Verständnis für die existierenden Glättungsmethoden führt uns dieser Ansatz zur Entwicklung des Verfahrens der 'empirischen Glättung'. Die damit durchgeführten Retrievalerexperimente zeigen Verbesserungen gegenüber anderen Glättungsverfahren.Language Models and Smoothing Methods for Information Retrieval Najeeb A. Abdulmutalib Abstract of the Dissertation Designing an effective retrieval model that can rank documents accurately for a given query has been a central problem in information retrieval for several decades. An optimal retrieval model that is both effective and efficient and that can learn from feedback information over time is needed. Language models are new generation of retrieval models and have been applied since the last ten years to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, they can be more easily adapted to model non traditional and complex retrieval problems and empirically they tend to achieve comparable or better performance than the traditional models. Developing new language models is currently an active research area in information retrieval. In the first stage of this thesis we present a new language model based on an odds formula, which explicitly incorporates document length as a parameter. To address the problem of data sparsity where there is rarely enough data to accurately estimate the parameters of a language model, smoothing gives a way to combine less specific, more accurate information with more specific, but noisier data. We introduce a new smoothing method called exponential smoothing, which can be combined with most language models. We present experimental results for various language models and smoothing methods on a collection with large document length variation, and show that our new methods compare favourably with the best approaches known so far. We discuss the collection effect on the retrieval function, where we investigate the performance of well known models and compare the results conducted using two variant collections. In the second stage we extend the current model from flat text retrieval to XML retrieval since there is a need for content-oriented XML retrieval systems that can efficiently and effectively store, search and retrieve information from XML document collections. Compared to traditional information retrieval, where whole documents are usually indexed and retrieved as single complete units, information retrieval from XML documents creates additional retrieval challenges. By exploiting the logical document structure, XML allows for more focussed retrieval that identifies elements rather than documents as answers to user queries. Finally we show how smoothing plays a role very similar to that of the idf function: beside the obvious role of smoothing, it also improves the accuracy of the estimated language model. The within document frequency and the collection frequency of a term actually influence the probability of relevance, which led us to a new class of smoothing function based on numeric prediction, which we call empirical smoothing. Its retrieval quality outperforms that of other smoothing methods