@phdthesis{Zellhoefer2015, author = {Zellh{\"o}fer, David}, title = {A preference-based relevance feedback approach for polyrepresentative multimedia retrieval}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-35218}, school = {BTU Cottbus - Senftenberg}, year = {2015}, abstract = {The search for textual information, e.g., in the form of webpages, is a typical task in modern business and private life. From a user's point of view, the commonly used systems have matured and established common interaction design patterns such as the textual input box that starts virtually every directed search process. In comparison, the search for multimedia documents (e.g., images or videos) is still in its early years. In other words, a pre-dominant search strategy has not yet evolved. That is, directed and exploratory search approaches fight for user acceptance. One further discriminative factor of multimedia information retrieval (MMIR) from traditional text-based information retrieval (IR) is that multimedia documents are not necessarily stored with the help of the same data access paradigm. From a technical point of view, the use of different data access paradigms complicates the retrieval from such collections because the utilized retrieval model has to support these paradigms. As a consequence, the main challenges in MMIR - the retrieval engine and the user interaction -- have to be addressed in a holistic way. A holistic theoretic perspective on MMIR/IR research is taken by principle of polyrepresentation (PoP), which forms one half of the theoretic background of this dissertation aiming at the development of a preference-based approach to interactive MMIR. Roughly speaking, the PoP theorizes that representations describing a document are based on various cognitive processes dealing with it, e.g., a title, its color or shape features, its creator, or its date of creation. This multitude of representations can be fused to form a conjunctive cognitive overlap (CO) in which highly relevant documents are likely to be contained. This explicit recommendation discriminates the PoP from typical feature fusion approaches often used in MMIR. However, the PoP does not answer how a retrieval model has to be implemented in a technical sense which is of interest in the field of computer science. One possibility to implement the PoP are quantum mechanics-inspired IR models such as the commuting quantum query language (CQQL) which is used in this thesis. CQQL is particularly interesting because it integrates data access paradigms used in the fields of DB and IR. In order to respect the dynamic nature of the search process and information need (IN), CQQL allows the personalization of retrieval results using a preference-based relevance feedback (RF) approach called PrefCQQL, which relies on machine-based learning. Unique features of the PrefCQQL approach range from the support of negative query-by-example (QBE) documents at query formulation time as well as during the interactive retrieval process to the formulation of weak preferences between result documents to express gradual levels of relevance. In addition, inductive preferences can be used from query formulation time onward to learn new CQQL queries. In order to evaluate the presented polyrepresentative PrefCQQL approach, two kinds of experiments are conducted: a Cranfield-inspired evaluation of CQQL/PrefCQQL's retrieval effectiveness, which is extended by the utilization of user simulations to better fit the requirements of the evaluation of an adaptive IR system, and a usability study that examines three alternative MMIR system UI prototypes. In order to increase the reproducibility and confirmability of the experiments, the source code to all used programs is made available as a supplement to this dissertation. The mentioned experiments aim at answering two central questions: first, whether the hypotheses of the PoP can be verified in MMIR, and second, whether a usable interactive MMIR system can be built on the basis of the PoP and PrefCQQL? To answer the first question, different matching functions that partly follow the recommendations of the PoP are evaluated with six different test collections in both an non-interactive and interactive QBE scenario. The results of this experiment are ambivalent. In non-interactive MMIR, the experimental data does not provide sufficient justification for the statement that PoP-based matching functions will always surpass single features or other matching functions. For instance, the arithmetic mean, which calculates the average similarity between a query's representations and the documents' representations in the collection, surpasses the conjunction and hence the CO of multiple representations in terms of retrieval effectiveness. Nevertheless, the matching function following the PoP is effectiveness stabler than the best performing single representations per collection. Hence, the CO's retrieval performance is more reliable than the usage of single representations. In contrast, the predictions of the PoP can be verified in the investigated PrefCQQL-based interactive MMIR scenario. However, it is important to note that also the number of available representations has an impact on the retrieval outcome. That is, if too few representations are present in a matching function, the corresponding IN model in PrefCQQL obviously becomes subject to underfitting eventually lowering its retrieval effectiveness. Unfortunately, when the point of sufficient representations to support PrefCQQL is reached could not be revealed in this dissertation. The second question is answered with the help of a prototypical MMIR system: the Pythia system, which serves both as proof of concept of the CQQL and the PrefCQQL approach. Furthermore, the system supports different information seeking strategies and a seamless transition between them in order to support users with different kinds of IN.}, subject = {Quantum logic-based information retrieval; Interactive multimedia retrieval; Principle of polyrepresentation; Content-based image retrieval; Feature fusion; Content-based Image Retrieval; Feature Fusion; Interaktives Multimedia Retrieval; Prinzip der Polyrepr{\"a}sentation; Quantenlogik-basiertes Information Retrieval; Multimedia; Information Retrieval}, language = {en} }