Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning

Distributional word representation methods exploit word co-occurrences to build compact vector encodings of words. While these representations enjoy widespread use in modern natural language processing, it is unclear whether they accurately encode all necessary facets of conceptual meaning. In this paper, we evaluate how well these representations can predict perceptual and conceptual features of concrete concepts, drawing on two semantic norm datasets sourced from human participants. We find that several standard word representations fail to encode many salient perceptual features of concepts, and show that these deficits correlate with word-word similarity prediction errors. Our analyses provide motivation for grounded and embodied language learning approaches, which may help to remedy these deficits.Comment: Accepted at RoboNLP 201

Building a semantic search engine with games and crowdsourcing

Semantic search engines aim at improving conventional search with semantic information, or meta-data, on the data searched for and/or on the searchers. So far, approaches to semantic search exploit characteristics of the searchers like age, education, or spoken language for selecting and/or ranking search results. Such data allow to build up a semantic search engine as an extension of a conventional search engine. The crawlers of well established search engines like Google, Yahoo! or Bing can index documents but, so far, their capabilities to recognize the intentions of searchers are still rather limited. Indeed, taking into account characteristics of the searchers considerably extend both, the quantity of data to analyse and the dimensionality of the search problem. Well established search engines therefore still focus on general search, that is, "search for all", not on specialized search, that is, "search for a few". This thesis reports on techniques that have been adapted or conceived, deployed, and tested for building a semantic search engine for the very specific context of artworks. In contrast to, for example, the interpretation of X-ray images, the interpretation of artworks is far from being fully automatable. Therefore artwork interpretation has been based on Human Computation, that is, a software-based gathering of contributions by many humans. The approach reported about in this thesis first relies on so called Games With A Purpose, or GWAPs, for this gathering: Casual games provide an incentive for a potentially unlimited community of humans to contribute with their appreciations of artworks. Designing convenient incentives is less trivial than it might seem at first. An ecosystem of games is needed so as to collect the meta-data on artworks intended for. One game generates the data that can serve as input of another game. This results in semantically rich meta-data that can be used for building up a successful semantic search engine. Thus, a first part of this thesis reports on a "game ecosystem" specifically designed from one known game and including several novel games belonging to the following game classes: (1) Description Games for collecting obvious and trivial meta-data, basically the well-known ESP (for extra-sensorial perception) game of Luis von Ahn, (2) the Dissemination Game Eligo generating translations, (3) the Diversification Game Karido aiming at sharpening differences between the objects, that is, the artworks, interpreted and (3) the Integration Games Combino, Sentiment and TagATag that generate structured meta-data. Secondly, the approach to building a semantic search engine reported about in this thesis relies on Higher-Order Singular Value Decomposition (SVD). More precisely, the data and meta-data on artworks gathered with the afore mentioned GWAPs are collected in a tensor, that is a mathematical structure generalising matrices to more than only two dimensions, columns and rows. The dimensions considered are the artwork descriptions, the players, and the artwork themselves. A Higher-Order SVD of this tensor is first used for noise reduction in This thesis reports also on deploying a Higher-Order LSA. The parallel Higher-Order SVD algorithm applied for the Higher-Order LSA and its implementation has been validated on an application related to, but independent from, the semantic search engine for artworks striven for: image compression. This thesis reports on the surprisingly good image compression which can be achieved with Higher-Order SVD. While compression methods based on matrix SVD for each color, the approach reported about in this thesis relies on one single (higher-order) SVD of the whole tensor. This results in both, better quality of the compressed image and in a significant reduction of the memory space needed. Higher-Order SVD is extremely time-consuming what calls for parallel computation. Thus, a step towards automatizing the construction of a semantic search engine for artworks was parallelizing the higher-order SVD method used and running the resulting parallel algorithm on a super-computer. This thesis reports on using Hestenes’ method and R-SVD for parallelising the higher-order SVD. This method is an unconventional choice which is explained and motivated. As of the super-computer needed, this thesis reports on turning the web browsers of the players or searchers into a distributed parallel computer. This is done by a novel specific system and a novel implementation of the MapReduce data framework to data parallelism. Harnessing the web browsers of the players or searchers saves computational power on the server-side. It also scales extremely well with the number of players or searchers because both, playing with and searching for artworks, require human reflection and therefore results in idle local processors that can be brought together into a distributed super-computer.Semantische Suchmaschinen dienen der Verbesserung konventioneller Suche mit semantischen Informationen, oder Metadaten, zu Daten, nach denen gesucht wird, oder zu den Suchenden. Bisher nutzt Semantische Suche Charakteristika von Suchenden wie Alter, Bildung oder gesprochene Sprache für die Auswahl und/oder das Ranking von Suchergebnissen. Solche Daten erlauben den Aufbau einer Semantischen Suchmaschine als Erweiterung einer konventionellen Suchmaschine. Die Crawler der fest etablierten Suchmaschinen wie Google, Yahoo! oder Bing können Dokumente indizieren, bisher sind die Fähigkeiten eher beschränkt, die Absichten von Suchenden zu erkennen. Tatsächlich erweitert die Berücksichtigung von Charakteristika von Suchenden beträchtlich beides, die Menge an zu analysierenden Daten und die Dimensionalität des Such-Problems. Fest etablierte Suchmaschinen fokussieren deswegen stark auf allgemeine Suche, also "Suche für alle", nicht auf spezialisierte Suche, also "Suche für wenige". Diese Arbeit berichtet von Techniken, die adaptiert oder konzipiert, eingesetzt und getestet wurden, um eine semantische Suchmaschine für den sehr speziellen Kontext von Kunstwerken aufzubauen. Im Gegensatz beispielsweise zur Interpretation von Röntgenbildern ist die Interpretation von Kunstwerken weit weg davon gänzlich automatisiert werden zu können. Deswegen basiert die Interpretation von Kunstwerken auf menschlichen Berechnungen, also Software-basiertes Sammeln von menschlichen Beiträgen. Der Ansatz, über den in dieser Arbeit berichtet wird, beruht auf sogenannten "Games With a Purpose" oder GWAPs die folgendes sammeln: Zwanglose Spiele bieten einen Anreiz für eine potenziell unbeschränkte Gemeinde von Menschen, mit Ihrer Wertschätzung von Kunstwerken beizutragen. Geeignete Anreize zu entwerfen in weniger trivial als es zuerst scheinen mag. Ein Ökosystem von Spielen wird benötigt, um Metadaten gedacht für Kunstwerke zu sammeln. Ein Spiel erzeugt Daten, die als Eingabe für ein anderes Spiel dienen können. Dies resultiert in semantisch reichhaltigen Metadaten, die verwendet werden können, um eine erfolgreiche Semantische Suchmaschine aufzubauen. Deswegen berichtet der erste Teil dieser Arbeit von einem "Spiel-Ökosystem", entwickelt auf Basis eines bekannten Spiels und verschiedenen neuartigen Spielen, die zu verschiedenen Spiel-Klassen gehören. (1) Beschreibungs-Spiele zum Sammeln offensichtlicher und trivialer Metadaten, vor allem dem gut bekannten ESP-Spiel (Extra Sensorische Wahrnehmung) von Luis von Ahn, (2) dem Verbreitungs-Spiel Eligo zur Erzeugung von Übersetzungen, (3) dem Diversifikations-Spiel Karido, das Unterschiede zwischen Objekten, also interpretierten Kunstwerken, schärft und (3) Integrations-Spiele Combino, Sentiment und Tag A Tag, die strukturierte Metadaten erzeugen. Zweitens beruht der Ansatz zum Aufbau einer semantischen Suchmaschine, wie in dieser Arbeit berichtet, auf Singulärwertzerlegung (SVD) höherer Ordnung. Präziser werden die Daten und Metadaten über Kunstwerk gesammelt mit den vorher genannten GWAPs in einem Tensor gesammelt, einer mathematischen Struktur zur Generalisierung von Matrizen zu mehr als zwei Dimensionen, Spalten und Zeilen. Die betrachteten Dimensionen sind die Beschreibungen der Kunstwerke, die Spieler, und die Kunstwerke selbst. Eine Singulärwertzerlegung höherer Ordnung dieses Tensors wird zuerst zur Rauschreduktion verwendet nach der Methode der sogenannten Latenten Semantischen Analyse (LSA). Diese Arbeit berichtet auch über die Anwendung einer LSA höherer Ordnung. Der parallele Algorithmus für Singulärwertzerlegungen höherer Ordnung, der für LSA höherer Ordnung verwendet wird, und seine Implementierung wurden validiert an einer verwandten aber von der semantischen Suche unabhängig angestrebten Anwendung: Bildkompression. Diese Arbeit berichtet von überraschend guter Kompression, die mit Singulärwertzerlegung höherer Ordnung erzielt werden kann. Neben Matrix-SVD-basierten Kompressionsverfahren für jede Farbe, beruht der Ansatz wie in dieser Arbeit berichtet auf einer einzigen SVD (höherer Ordnung) auf dem gesamten Tensor. Dies resultiert in beidem, besserer Qualität von komprimierten Bildern und einer signifikant geringeren des benötigten Speicherplatzes. Singulärwertzerlegung höherer Ordnung ist extrem zeitaufwändig, was parallele Berechnung verlangt. Deswegen war ein Schritt in Richtung Aufbau einer semantischen Suchmaschine für Kunstwerke eine Parallelisierung der verwendeten SVD höherer Ordnung auf einem Super-Computer. Diese Arbeit berichtet vom Einsatz der Hestenes’-Methode und R-SVD zur Parallelisierung der SVD höherer Ordnung. Diese Methode ist eine unkonventionell Wahl, die erklärt und motiviert wird. Ab nun wird ein Super-Computer benötigt. Diese Arbeit berichtet über die Wandlung der Webbrowser von Spielern oder Suchenden in einen verteilten Super-Computer. Dies leistet ein neuartiges spezielles System und eine neuartige Implementierung des MapReduce Daten-Frameworks für Datenparallelismus. Das Einspannen der Webbrowser von Spielern und Suchenden spart server-seitige Berechnungskraft. Ebenso skaliert die Berechnungskraft so extrem gut mit der Spieleranzahl oder Suchenden, denn beides, Spiel mit oder Suche nach Kunstwerken, benötigt menschliche Reflektion, was deswegen zu ungenutzten lokalen Prozessoren führt, die zu einem verteilten Super-Computer zusammengeschlossen werden können

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen