Topic models for image retrieval on large scale databases [Elektronische Ressource] / Eva Hörster

DissertationTOPIC MODELS FOR IMAGE RETRIEVAL ONLARGE-SCALE DATABASESEva HörsterDepartment of Computer ScienceUniversity of AugsburgAdviser: Prof. Dr. Rainer LienhartReaders: Prof. Dr. Rainer LienhartProf. Dr. Bernhard MöllerProf. Dr. Wolfgang EffelsbergThesis Defense: July 14, 2009AbstractWith the explosion of the number of images in personal and on-line collections, efficient tech-niques for navigating, indexing, labeling and searching images become more and more impor-tant. In this work we will rely on the image content as the main source of information to retrieveimages. We study the representation of images by topic models in its various aspects and ex-tend the current models. Starting from a bag-of-visual-words image description based on localimage features, images representations are learned in an unsupervised fashion and each imageis modeled as a mixture of topics/object parts depicted in the image. Thus topic models allowus to automatically extract high-level image content descriptions which in turn can be used tofind similar images. Further, the typically low-dimensional topic-model-based representationenables efficient and fast search, especially in very large databases.In this thesis we present a complete image retrieval system based on topic models and evaluatethe suitability of different types of topic models for the task of large-scale retrieval on real-worlddatabases.