06171 Abstracts Collection -- Content-Based Retrieval

From 23.04.06 to 28.04.06, the Dagstuhl Seminar 06171 `Content-Based Retrieval\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Metadata

Metadata, or data about data, play a crucial rule in social sciences to ensure that high quality documentation and community knowledge are properly captured and surround the data across its entire life cycle, from the early stages of production to secondary analysis by researchers or use by policy makers and other key stakeholders. The paper provides an overview of the social sciences metadata landscape, best practices and related information technologies. It particularly focuses on two specifications - the Data Documentation Initiative (DDI) and the Statistical Data and Metadata Exchange Standard (SDMX) - seen as central to a global metadata management framework for social data and official statistics. It also highlights current directions, outlines typical integration challenges, and provides a set of high level recommendations for producers, archives, researchers and sponsors in order to foster the adoption of metadata standards and best practices in the years to come.social sciences, metadata, data, statistics, documentation, data quality, XML, DDI, SDMX, archive, preservation, production, access, dissemination, analysis

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Semantic Spaces for Video Analysis of Behaviour

PhDThere are ever growing interests from the computer vision community into human behaviour analysis based on visual sensors. These interests generally include: (1) behaviour recognition - given a video clip or specific spatio-temporal volume of interest discriminate it into one or more of a set of pre-defined categories; (2) behaviour retrieval - given a video or textual description as query, search for video clips with related behaviour; (3) behaviour summarisation - given a number of video clips, summarise out representative and distinct behaviours. Although countless efforts have been dedicated into problems mentioned above, few works have attempted to analyse human behaviours in a semantic space. In this thesis, we define semantic spaces as a collection of high-dimensional Euclidean space in which semantic meaningful events, e.g. individual word, phrase and visual event, can be represented as vectors or distributions which are referred to as semantic representations. With the semantic space, semantic texts, visual events can be quantitatively compared by inner product, distance and divergence. The introduction of semantic spaces can bring lots of benefits for visual analysis. For example, discovering semantic representations for visual data can facilitate semantic meaningful video summarisation, retrieval and anomaly detection. Semantic space can also seamlessly bridge categories and datasets which are conventionally treated independent. This has encouraged the sharing of data and knowledge across categories and even datasets to improve recognition performance and reduce labelling effort. Moreover, semantic space has the ability to generalise learned model beyond known classes which is usually referred to as zero-shot learning. Nevertheless, discovering such a semantic space is non-trivial due to (1) semantic space is hard to define manually. Humans always have a good sense of specifying the semantic relatedness between visual and textual instances. But a measurable and finite semantic space can be difficult to construct with limited manual supervision. As a result, constructing semantic space from data is adopted to learn in an unsupervised manner; (2) It is hard to build a universal semantic space, i.e. this space is always contextual dependent. So it is important to build semantic space upon selected data such that it is always meaningful within the context. Even with a well constructed semantic space, challenges are still present including; (3) how to represent visual instances in the semantic space; and (4) how to mitigate the misalignment of visual feature and semantic spaces across categories and even datasets when knowledge/data are generalised. This thesis tackles the above challenges by exploiting data from different sources and building contextual semantic space with which data and knowledge can be transferred and shared to facilitate the general video behaviour analysis. To demonstrate the efficacy of semantic space for behaviour analysis, we focus on studying real world problems including surveillance behaviour analysis, zero-shot human action recognition and zero-shot crowd behaviour recognition with techniques specifically tailored for the nature of each problem. Firstly, for video surveillances scenes, we propose to discover semantic representations from the visual data in an unsupervised manner. This is due to the largely availability of unlabelled visual data in surveillance systems. By representing visual instances in the semantic space, data and annotations can be generalised to new events and even new surveillance scenes. Specifically, to detect abnormal events this thesis studies a geometrical alignment between semantic representation of events across scenes. Semantic actions can be thus transferred to new scenes and abnormal events can be detected in an unsupervised way. To model multiple surveillance scenes simultaneously, we show how to learn a shared semantic representation across a group of semantic related scenes through a multi-layer clustering of scenes. With multi-scene modelling we show how to improve surveillance tasks including scene activity profiling/understanding, crossscene query-by-example, behaviour classification, and video summarisation. Secondly, to avoid extremely costly and ambiguous video annotating, we investigate how to generalise recognition models learned from known categories to novel ones, which is often termed as zero-shot learning. To exploit the limited human supervision, e.g. category names, we construct the semantic space via a word-vector representation trained on large textual corpus in an unsupervised manner. Representation of visual instance in semantic space is obtained by learning a visual-to-semantic mapping. We notice that blindly applying the mapping learned from known categories to novel categories can cause bias and deteriorating the performance which is termed as domain shift. To solve this problem we employed techniques including semisupervised learning, self-training, hubness correction, multi-task learning and domain adaptation. All these methods in combine achieve state-of-the-art performance in zero-shot human action task. In the last, we study the possibility to re-use known and manually labelled semantic crowd attributes to recognise rare and unknown crowd behaviours. This task is termed as zero-shot crowd behaviours recognition. Crucially we point out that given the multi-labelled nature of semantic crowd attributes, zero-shot recognition can be improved by exploiting the co-occurrence between attributes. To summarise, this thesis studies methods for analysing video behaviours and demonstrates that exploring semantic spaces for video analysis is advantageous and more importantly enables multi-scene analysis and zero-shot learning beyond conventional learning strategies

High-Level Facade Image Interpretation using Marked Point Processes

In this thesis, we address facade image interpretation as one essential ingredient for the generation of high-detailed, semantic meaningful, three-dimensional city-models. Given a single rectified facade image, we detect relevant facade objects such as windows, entrances, and balconies, which yield a description of the image in terms of accurate position and size of these objects. Urban digital three-dimensional reconstruction and documentation is an active area of research with several potential applications, e.g., in the area of digital mapping for navigation, urban planning, emergency management, disaster control or the entertainment industry. A detailed building model which is not just a geometric object enriched with texture, allows for semantic requests as the number of floors or the location of balconies and entrances. Facade image interpretation is one essential step in order to yield such models. In this thesis, we propose the interpretation of facade images by combining evidence for the occurrence of individual object classes which we derive from data, and prior knowledge which guides the image interpretation in its entirety. We present a three-step procedure which generates features that are suited to describe relevant objects, learns a representation that is suited for object detection, and that enables the image interpretation using the results of object detection while incorporating prior knowledge about typical configurations of facade objects, which we learn from training data. According to these three sub-tasks, our major achievements are: We propose a novel method for facade image interpretation based on a marked point process. Therefor, we develop a model for the description of typical configurations of facade objects and propose an image interpretation system which combines evidence derived from data and prior knowledge about typical configurations of facade objects. In order to generate evidence from data, we propose a feature type which we call shapelets. They are scale invariant and provide large distinctiveness for facade objects. Segments of lines, arcs, and ellipses serve as basic features for the generation of shapelets. Therefor, we propose a novel line simplification approach which approximates given pixel-chains by a sequence of lines, circular, and elliptical arcs. Among others, it is based on an adaption to Douglas-Peucker's algorithm, which is based on circles as basic geometric elements We evaluate each step separately. We show the effects of polyline segmentation and simplification on several images with comparable good or even better results, referring to a state-of-the-art algorithm, which proves their large distinctiveness for facade objects. Using shapelets we provide a reasonable classification performance on a challenging dataset, including intra-class variations, clutter, and scale changes. Finally, we show promising results for the facade interpretation system on several datasets and provide a qualitative evaluation which demonstrates the capability of complete and accurate detection of facade objectsHigh-Level Interpretation von Fassaden-Bildern unter Benutzung von Markierten PunktprozessenDas Thema dieser Arbeit ist die Interpretation von Fassadenbildern als wesentlicher Beitrag zur Erstellung hoch detaillierter, semantisch reichhaltiger dreidimensionaler Stadtmodelle. In rektifizierten Einzelaufnahmen von Fassaden detektieren wir relevante Objekte wie Fenster, Türen und Balkone, um daraus eine Bildinterpretation in Form von präzisen Positionen und Größen dieser Objekte abzuleiten. Die digitale dreidimensionale Rekonstruktion urbaner Regionen ist ein aktives Forschungsfeld mit zahlreichen Anwendungen, beispielsweise der Herstellung digitaler Kartenwerke für Navigation, Stadtplanung, Notfallmanagement, Katastrophenschutz oder die Unterhaltungsindustrie. Detaillierte Gebäudemodelle, die nicht nur als geometrische Objekte repräsentiert und durch eine geeignete Textur visuell ansprechend dargestellt werden, erlauben semantische Anfragen, wie beispielsweise nach der Anzahl der Geschosse oder der Position der Balkone oder Eingänge. Die semantische Interpretation von Fassadenbildern ist ein wesentlicher Schritt für die Erzeugung solcher Modelle. In der vorliegenden Arbeit lösen wir diese Aufgabe, indem wir aus Daten abgeleitete Evidenz für das Vorkommen einzelner Objekte mit Vorwissen kombinieren, das die Analyse der gesamten Bildinterpretation steuert. Wir präsentieren dafür ein dreistufiges Verfahren: Wir erzeugen Bildmerkmale, die für die Beschreibung der relevanten Objekte geeignet sind. Wir lernen, auf Basis abgeleiteter Merkmale, eine Repräsentation dieser Objekte. Schließlich realisieren wir die Bildinterpretation basierend auf der zuvor gelernten Repräsentation und dem Vorwissen über typische Konfigurationen von Fassadenobjekten, welches wir aus Trainingsdaten ableiten. Wir leisten dazu die folgenden wissenschaftlichen Beiträge: Wir schlagen eine neuartige Me-thode zur Interpretation von Fassadenbildern vor, die einen sogenannten markierten Punktprozess verwendet. Dafür entwickeln wir ein Modell zur Beschreibung typischer Konfigurationen von Fassadenobjekten und entwickeln ein Bildinterpretationssystem, welches aus Daten abgeleitete Evidenz und a priori Wissen über typische Fassadenkonfigurationen kombiniert. Für die Erzeugung der Evidenz stellen wir Merkmale vor, die wir Shapelets nennen und die skaleninvariant und durch eine ausgesprochene Distinktivität im Bezug auf Fassadenobjekte gekennzeichnet sind. Als Basismerkmale für die Erzeugung der Shapelets dienen Linien-, Kreis- und Ellipsensegmente. Dafür stellen wir eine neuartige Methode zur Vereinfachung von Liniensegmenten vor, die eine Pixelkette durch eine Sequenz von geraden Linienstücken und elliptischen Bogensegmenten approximiert. Diese basiert unter anderem auf einer Adaption des Douglas-Peucker Algorithmus, die anstelle gerader Linienstücke, Bogensegmente als geometrische Basiselemente verwendet. Wir evaluieren jeden dieser drei Teilschritte separat. Wir zeigen Ergebnisse der Liniensegmen-tierung anhand verschiedener Bilder und weisen dabei vergleichbare und teilweise verbesserte Ergebnisse im Vergleich zu bestehende Verfahren nach. Für die vorgeschlagenen Shapelets weisen wir in der Evaluation ihre diskriminativen Eigenschaften im Bezug auf Fassadenobjekte nach. Wir erzeugen auf einem anspruchsvollen Datensatz von skalenvariablen Fassadenobjekten, mit starker Variabilität der Erscheinung innerhalb der Klassen, vielversprechende Klassifikationsergebnisse, die die Verwendbarkeit der gelernten Shapelets für die weitere Interpretation belegen. Schließlich zeigen wir Ergebnisse der Interpretation der Fassadenstruktur anhand verschiedener Datensätze. Die qualitative Evaluation demonstriert die Fähigkeit des vorgeschlagenen Lösungsansatzes zur vollständigen und präzisen Detektion der genannten Fassadenobjekte

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert