Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques

Topological Foundations of Cognitive Science

A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers: ** Topological Foundations of Cognitive Science, Barry Smith ** The Bounds of Axiomatisation, Graham White ** Rethinking Boundaries, Wojciech Zelaniec ** Sheaf Mereology and Space Cognition, Jean Petitot ** A Mereotopological Definition of 'Point', Carola Eschenbach ** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel ** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda ** Defining a 'Doughnut' Made Difficult, N .M. Gotts ** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts ** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi ** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Ki

Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud \ud \ud KEYWORDS: Emergence of Life and Human Consciousness;\ud Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud \u

Positive psychology and tourism: a systematic literature review

This study aims to outline the relationship between Positive Psychology and tourism through a systematic literature review. Tourism seeks to increase the wellbeing of people, and wellbeing is a crucial variable in Positive Psychology which in turn aims to understand and promote people's potential. This research used as search terms 'Positive Psychology', 'wellbeing', 'happiness', 'tourism', 'visitor' and 'travel', terms which were applied through the Online Knowledge Library. The inclusion/exclusion criteria led to a sample of 49 references which were then individually analyzed. Results showed a recent increase in studies focused on the relationship between the variables, Europe being in the lead. Overall, policies are important for tourism development; tourism promotes wellbeing for residents and tourists; entrepreneurs have an innovative opportunity in wellbeing; and nature is linked to wellbeing. Implications and suggestions for future studies are presented.Agência financiadora ARDITI - Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação: M1420-09-5369-FSE-000001info:eu-repo/semantics/publishedVersio

Experience-based language acquisition: a computational model of human language acquisition

Almost from the very beginning of the digital age, people have sought better ways to communicate with computers. This research investigates how computers might be enabled to understand natural language in a more humanlike way. Based, in part, on cognitive development in infants, we introduce an open computational framework for visual perception and grounded language acquisition called Experience-Based Language Acquisition (EBLA). EBLA can “watch” a series of short videos and acquire a simple language of nouns and verbs corresponding to the objects and object-object relations in those videos. Upon acquiring this protolanguage, EBLA can perform basic scene analysis to generate descriptions of novel videos. The general architecture of EBLA is comprised of three stages: vision processing, entity extraction, and lexical resolution. In the vision processing stage, EBLA processes the individual frames in short videos, using a variation of the mean shift analysis image segmentation algorithm to identify and store information about significant objects. In the entity extraction stage, EBLA abstracts information about the significant objects in each video and the relationships among those objects into internal representations called entities. Finally, in the lexical acquisition stage, EBLA extracts the individual lexemes (words) from simple descriptions of each video and attempts to generate entity-lexeme mappings using an inference technique called cross-situational learning. EBLA is not primed with a base lexicon, so it faces the task of bootstrapping its lexicon from scratch. The performance of EBLA has been evaluated based on acquisition speed and accuracy of scene descriptions. For a test set of simple animations, EBLA had average acquisition success rates as high as 100% and average description success rates as high as 96.7%. For a larger set of real videos, EBLA had average acquisition success rates as high as 95.8% and average description success rates as high as 65.3%. The lower description success rate for the videos is attributed to the wide variance in entities across the videos. While there have been several systems capable of learning object or event labels for videos, EBLA is the first known system to acquire both nouns and verbs using a grounded computer vision system

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

Image Retrieval within Augmented Reality

Die vorliegende Arbeit untersucht das Potenzial von Augmented Reality zur Verbesserung von Image Retrieval Prozessen. Herausforderungen in Design und Gebrauchstauglichkeit wurden für beide Forschungsbereiche dargelegt und genutzt, um Designziele für Konzepte zu entwerfen. Eine Taxonomie für Image Retrieval in Augmented Reality wurde basierend auf der Forschungsarbeit entworfen und eingesetzt, um verwandte Arbeiten und generelle Ideen für Interaktionsmöglichkeiten zu strukturieren. Basierend auf der Taxonomie wurden Anwendungsszenarien als weitere Anforderungen für Konzepte formuliert. Mit Hilfe der generellen Ideen und Anforderungen wurden zwei umfassende Konzepte für Image Retrieval in Augmented Reality ausgearbeitet. Eins der Konzepte wurde auf einer Microsoft HoloLens umgesetzt und in einer Nutzerstudie evaluiert. Die Studie zeigt, dass das Konzept grundsätzlich positiv aufgenommen wurde und bietet Erkenntnisse über unterschiedliches Verhalten im Raum und verschiedene Suchstrategien bei der Durchführung von Image Retrieval in der erweiterten Realität.:1 Introduction 1.1 Motivation and Problem Statement 1.1.1 Augmented Reality and Head-Mounted Displays 1.1.2 Image Retrieval 1.1.3 Image Retrieval within Augmented Reality 1.2 Thesis Structure 2 Foundations of Image Retrieval and Augmented Reality 2.1 Foundations of Image Retrieval 2.1.1 Definition of Image Retrieval 2.1.2 Classification of Image Retrieval Systems 2.1.3 Design and Usability in Image Retrieval 2.2 Foundations of Augmented Reality 2.2.1 Definition of Augmented Reality 2.2.2 Augmented Reality Design and Usability 2.3 Taxonomy for Image Retrieval within Augmented Reality 2.3.1 Session Parameters 2.3.2 Interaction Process 2.3.3 Summary of the Taxonomy 3 Concepts for Image Retrieval within Augmented Reality 3.1 Related Work 3.1.1 Natural Query Specification 3.1.2 Situated Result Visualization 3.1.3 3D Result Interaction 3.1.4 Summary of Related Work 3.2 Basic Interaction Concepts for Image Retrieval in Augmented Reality 3.2.1 Natural Query Specification 3.2.2 Situated Result Visualization 3.2.3 3D Result Interaction 3.3 Requirements for Comprehensive Concepts 3.3.1 Design Goals 3.3.2 Application Scenarios 3.4 Comprehensive Concepts 3.4.1 Tangible Query Workbench 3.4.2 Situated Photograph Queries 3.4.3 Conformance of Concept Requirements 4 Prototypic Implementation of Situated Photograph Queries 4.1 Implementation Design 4.1.1 Implementation Process 4.1.2 Structure of the Implementation 4.2 Developer and User Manual 4.2.1 Setup of the Prototype 4.2.2 Usage of the Prototype 4.3 Discussion of the Prototype 5 Evaluation of Prototype and Concept by User Study 5.1 Design of the User Study 5.1.1 Usability Testing 5.1.2 Questionnaire 5.2 Results 5.2.1 Logging of User Behavior 5.2.2 Rating through Likert Scales 5.2.3 Free Text Answers and Remarks during the Study 5.2.4 Observations during the Study 5.2.5 Discussion of Results 6 Conclusion 6.1 Summary of the Present Work 6.2 Outlook on Further WorkThe present work investigates the potential of augmented reality for improving the image retrieval process. Design and usability challenges were identified for both fields of research in order to formulate design goals for the development of concepts. A taxonomy for image retrieval within augmented reality was elaborated based on research work and used to structure related work and basic ideas for interaction. Based on the taxonomy, application scenarios were formulated as further requirements for concepts. Using the basic interaction ideas and the requirements, two comprehensive concepts for image retrieval within augmented reality were elaborated. One of the concepts was implemented using a Microsoft HoloLens and evaluated in a user study. The study showed that the concept was rated generally positive by the users and provided insight in different spatial behavior and search strategies when practicing image retrieval in augmented reality.:1 Introduction 1.1 Motivation and Problem Statement 1.1.1 Augmented Reality and Head-Mounted Displays 1.1.2 Image Retrieval 1.1.3 Image Retrieval within Augmented Reality 1.2 Thesis Structure 2 Foundations of Image Retrieval and Augmented Reality 2.1 Foundations of Image Retrieval 2.1.1 Definition of Image Retrieval 2.1.2 Classification of Image Retrieval Systems 2.1.3 Design and Usability in Image Retrieval 2.2 Foundations of Augmented Reality 2.2.1 Definition of Augmented Reality 2.2.2 Augmented Reality Design and Usability 2.3 Taxonomy for Image Retrieval within Augmented Reality 2.3.1 Session Parameters 2.3.2 Interaction Process 2.3.3 Summary of the Taxonomy 3 Concepts for Image Retrieval within Augmented Reality 3.1 Related Work 3.1.1 Natural Query Specification 3.1.2 Situated Result Visualization 3.1.3 3D Result Interaction 3.1.4 Summary of Related Work 3.2 Basic Interaction Concepts for Image Retrieval in Augmented Reality 3.2.1 Natural Query Specification 3.2.2 Situated Result Visualization 3.2.3 3D Result Interaction 3.3 Requirements for Comprehensive Concepts 3.3.1 Design Goals 3.3.2 Application Scenarios 3.4 Comprehensive Concepts 3.4.1 Tangible Query Workbench 3.4.2 Situated Photograph Queries 3.4.3 Conformance of Concept Requirements 4 Prototypic Implementation of Situated Photograph Queries 4.1 Implementation Design 4.1.1 Implementation Process 4.1.2 Structure of the Implementation 4.2 Developer and User Manual 4.2.1 Setup of the Prototype 4.2.2 Usage of the Prototype 4.3 Discussion of the Prototype 5 Evaluation of Prototype and Concept by User Study 5.1 Design of the User Study 5.1.1 Usability Testing 5.1.2 Questionnaire 5.2 Results 5.2.1 Logging of User Behavior 5.2.2 Rating through Likert Scales 5.2.3 Free Text Answers and Remarks during the Study 5.2.4 Observations during the Study 5.2.5 Discussion of Results 6 Conclusion 6.1 Summary of the Present Work 6.2 Outlook on Further Wor

A cognitive exploration of the “non-visual” nature of geometric proofs

Why are Geometric Proofs (Usually) “Non-Visual”? We asked this question as a way to explore the similarities and differences between diagrams and text (visual thinking versus language thinking). Traditional text-based proofs are considered (by many to be) more rigorous than diagrams alone. In this paper we focus on human perceptual-cognitive characteristics that may encourage textual modes for proofs because of the ergonomic affordances of text relative to diagrams. We suggest that visual-spatial perception of physical objects, where an object is perceived with greater acuity through foveal vision rather than peripheral vision, is similar to attention navigating a conceptual visual-spatial structure. We suggest that attention has foveal-like and peripheral-like characteristics and that textual modes appeal to what we refer to here as foveal-focal attention, an extension of prior work in focused attention

Visual saliency computation for image analysis

Visual saliency computation is about detecting and understanding salient regions and elements in a visual scene. Algorithms for visual saliency computation can give clues to where people will look in images, what objects are visually prominent in a scene, etc. Such algorithms could be useful in a wide range of applications in computer vision and graphics. In this thesis, we study the following visual saliency computation problems. 1) Eye Fixation Prediction. Eye fixation prediction aims to predict where people look in a visual scene. For this problem, we propose a Boolean Map Saliency (BMS) model which leverages the global surroundedness cue using a Boolean map representation. We draw a theoretic connection between BMS and the Minimum Barrier Distance (MBD) transform to provide insight into our algorithm. Experiment results show that BMS compares favorably with state-of-the-art methods on seven benchmark datasets. 2) Salient Region Detection. Salient region detection entails computing a saliency map that highlights the regions of dominant objects in a scene. We propose a salient region detection method based on the Minimum Barrier Distance (MBD) transform. We present a fast approximate MBD transform algorithm with an error bound analysis. Powered by this fast MBD transform algorithm, our method can run at about 80 FPS and achieve state-of-the-art performance on four benchmark datasets. 3) Salient Object Detection. Salient object detection targets at localizing each salient object instance in an image. We propose a method using a Convolutional Neural Network (CNN) model for proposal generation and a novel subset optimization formulation for bounding box filtering. In experiments, our subset optimization formulation consistently outperforms heuristic bounding box filtering baselines, such as Non-maximum Suppression, and our method substantially outperforms previous methods on three challenging datasets. 4) Salient Object Subitizing. We propose a new visual saliency computation task, called Salient Object Subitizing, which is to predict the existence and the number of salient objects in an image using holistic cues. To this end, we present an image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that an end-to-end trained CNN subitizing model can achieve promising performance without requiring any localization process. A method is proposed to further improve the training of the CNN subitizing model by leveraging synthetic images. 5) Top-down Saliency Detection. Unlike the aforementioned tasks, top-down saliency detection entails generating task-specific saliency maps. We propose a weakly supervised top-down saliency detection approach by modeling the top-down attention of a CNN image classifier. We propose Excitation Backprop and the concept of contrastive attention to generate highly discriminative top-down saliency maps. Our top-down saliency detection method achieves superior performance in weakly supervised localization tasks on challenging datasets. The usefulness of our method is further validated in the text-to-region association task, where our method provides state-of-the-art performance using only weakly labeled web images for training