Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semantic visualization with hyperbolic self-organizing maps : a novel approach for exploring structure in large data sets

Ontrup J. Semantic visualization with hyperbolic self-organizing maps : a novel approach for exploring structure in large data sets. Bielefeld (Germany): Bielefeld University; 2008.This thesis describes a novel semantic visualization approach for the exploration of structure in large data sets. The ever increasing amount of online data has led to an information overload which can be alleviated with techniques from information retrieval, machine learning, visualization and semantic processing. The thesis introduces a hierarchically growing variant of the self-organizing map where the geometrical lattice structure is constructed in hyperbolic space allowing a speed-up of several orders of magnitude for the learning of large maps. Furthermore a semantically guided extension to the classic bag-of-words model is given: WordNet is used to construct a hierarchical feature representation of documents, called the pyramid-of-words. In addition to the theoretical foundation of the aforementioned novel approaches, the architecture of a demonstrator system is introduced. The system is applied to several artificial data sets and three real world examples including the Reuters-21578 benchmark data set. The thesis closes with a user study addressing the question how effective the proposed system is with respect to navigation tasks in large data structures

Maschinelles Lernen und semantische Visualisierung - ein Beispiel zur Analyse von Nutzerfragen aus einem Verbraucherschutzforum

Internet-Foren sind allgegenwärtig. Menschen diskutieren online über neueste Nachrichten, die Eigenschaften neuer Digitalkameras oder fragen nach Ernährungstipps. Die Daten sind häufig online verfügbar, in den meisten Fällen jedoch völlig unstrukturiert. Eine Analyse oder Online-Monitoring gestaltet sich schwierig. Im Vortrag wird ein maschinelles Lernverfahren vorgestellt, mit dessen Hilfe große Mengen unstrukturierter Daten nach dem Prinzip der Selbsorganisation so geordnet werden können, dass eine semantische Visualiserung Einblicke in die Zusammenhänge der Daten erlaubt. Das Verfahren wird z.Zt. in Kooperation mit dem aid infodienst, einem vom Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz gefördertem e.V. angewendet. Beispiele aus der Praxis verdeutlichen den Einsatz des Systems

Maschinelles Lernen und semantische Visualisierung - ein Beispiel zur Analyse von Nutzerfragen aus einem Verbraucherschutzforum

Internet-Foren sind allgegenwärtig. Menschen diskutieren online über neueste Nachrichten, die Eigenschaften neuer Digitalkameras oder fragen nach Ernährungstipps. Die Daten sind häufig online verfügbar, in den meisten Fällen jedoch völlig unstrukturiert. Eine Analyse oder Online-Monitoring gestaltet sich schwierig. Im Vortrag wird ein maschinelles Lernverfahren vorgestellt, mit dessen Hilfe große Mengen unstrukturierter Daten nach dem Prinzip der Selbsorganisation so geordnet werden können, dass eine semantische Visualiserung Einblicke in die Zusammenhänge der Daten erlaubt. Das Verfahren wird z.Zt. in Kooperation mit dem aid infodienst, einem vom Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz gefördertem e.V. angewendet. Beispiele aus der Praxis verdeutlichen den Einsatz des Systems

Large-Scale Data Exploration with the Hierarchically Growing Hyperbolic SOM Abstract

We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H 2 SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that allows for incremental training with an automated adaptation of lattice size to achieve a prescribed quantization error and (ii) an approximate best match search that utilizes the special structure of the hyperbolic lattice to achieve a tremendous speed-up for large map sizes. Using the MNIST and the Reuters-21578 database as benchmark datasets, we show that the H 2 SOM yields a highly efficient visualization algorithm that combines the virtues of the SOM with extremely rapid training and low quantization and classification errors

Abstract

We introduce a new type of Self-Organizing Map (SOM) to navigate in the Semantic Space of large text collections. We propose a “hyperbolic SOM ” (HSOM) based on a regular tesselation of the hyperbolic plane, which is a non-euclidean space characterized by constant negative gaussian curvature. The exponentially increasing size of a neighborhood around a point in hyperbolic space provides more freedom to map the complex information space arising from language into spatial relations. We describe experiments, showing that the HSOM can successfully be applied to text categorization tasks and yields results comparable to other state-of-the-art methods.

A computational feature binding model of human texture perception

We present a computational model for human texture perception which assigns functional principles to the Gestalt laws of similarity and proximity. Motivated by early vision mechanisms, in a first step local texture features are extracted by utilizing multi-scale filtering and non-linear spatial pooling. In the second stage, features are grouped according to the spatial feature binding model of the Competitive Layer Model (CLM) (Wersing, Steil, & Ritter, 2001). The CLM uses cooperative and competitive interactions in a recurrent network, where binding is expressed by the layer-wise coactivation of feature-representing neurons. feature space with proximity being taken into account by a spatial component. To choose the stimulus dimensions which allow the most salient similarity-based texture segmentation, the feature similarity metrics is reduced to the directions of maximum variance. We show that our combined texture feature extraction and binding model performs segmentation in strong conformity with human perception. The examples range from classical microtextures and Brodatz textures to other classical Gestalt stimuli, which offer a new perspective on the role of texture for more abstract similarity grouping

Detecting, Assessing, and Monitoring Relevant Topics in Virtual Information Environments

Ontrup J, Ritter H, Scholz S, Wagner R. Detecting, Assessing, and Monitoring Relevant Topics in Virtual Information Environments. IEEE Transactions on Knowledge and Data Engineering. 2009;21(3):415-427.The ability to assess the relevance of topics and related sources in information-rich environments is a key to success when scanning business environments. This paper introduces a hybrid system to support managerial information gathering. The system is made up of three components: 1) a hierarchical hyperbolic SOM for structuring the information environment and visualizing the intensity of news activity with respect to identified topics, 2) a spreading activation network for the selection of the most relevant information sources with respect to an already existing knowledge infrastructure, and 3) measures of interestingness for association rules as well as statistical testing that facilitates the monitoring of already identified topics. Embedding the system by a framework describing three modes of human information seeking behavior endorses an active organization, exploration and selection of information that matches the needs of decision-makers in all stages of the information gathering process. By applying our system in the domain of the hotel industry, we demonstrate how typical information gathering tasks are supported. Moreover, we present an empirical study investigating the effectiveness and efficiency of the visualization framework of our system