32 research outputs found

    Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

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    The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.Comment: 8 pages, 16 figures, 11 table

    Content-based image retrieval based on relevance feedback and optimum-path forest classifier

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    Orientadores: LĂ©o Pini MagalhĂŁes, Alexandre Xavier FalcĂŁoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia ElĂ©trica e de ComputaçãoResumo: Com o crescente aumento de coleçÔes de imagens resultantes da popularização da Internet e das cĂąmeras digitais, mĂ©todos eficientes de busca tornam-se cada vez mais necessĂĄrios. Neste contexto, esta tese propĂ”e novos mĂ©todos de recuperação de imagens por conteĂșdo baseados em realimentação de relevĂąncia e no classificador por floresta de caminhos Ăłtimos (OPF - Optimum-Path Forest), sendo tambĂ©m a primeira vez que o classificador OPF Ă© utilizado em conjuntos de treinamento pequenos. Esta tese denomina como guloso e planejado os dois paradigmas distintos de aprendizagem por realimentação de relevĂąncia considerando as imagens retornadas. O primeiro paradigma tenta retornar a cada iteração sempre as imagens mais relevantes para o usuĂĄrio, enquanto o segundo utiliza no aprendizado as imagens consideradas mais informativas ou difĂ­ceis de classificar. SĂŁo apresentados os algoritmos de realimentação de relevĂąncia baseados em OPF utilizando ambos os paradigmas com descritor Ășnico. SĂŁo utilizadas tambĂ©m duas tĂ©cnicas de combinação de descritores juntamente com os mĂ©todos de realimentação de relevĂąncia baseados em OPF para melhorar a eficĂĄcia do processo de aprendizagem. A primeira, MSPS (Multi-Scale Parameter Search), Ă© utilizada pela primeira vez em recuperação de imagens por conteĂșdo, enquanto a segunda Ă© uma tĂ©cnica consolidada baseada em programação genĂ©tica. Uma nova abordagem para realimentação de relevĂąncia utilizando o classificador OPF em dois nĂ­veis de interesse Ă© tambĂ©m apresentada. Nesta abordagem Ă© possĂ­vel, em um nĂ­vel de interesse, selecionar os pixels nas imagens, alĂ©m de escolher as imagens mais relevantes a cada iteração no outro nĂ­vel. Esta tese mostra que o uso do classificador OPF para recuperação de imagens por conteĂșdo Ă© muito eficiente e eficaz, necessitando de poucas iteraçÔes de aprendizado para apresentar os resultados desejados aos usuĂĄrios. As simulaçÔes mostram que os mĂ©todos propostos superam os mĂ©todos de referĂȘncia baseados em mĂșltiplos pontos de consulta e em mĂĄquina de vetor de suporte (SVM). AlĂ©m disso, os mĂ©todos propostos de busca de imagens baseados no classificador por floresta de caminhos Ăłtimos mostraram ser em mĂ©dia 52 vezes mais rĂĄpidos do que os mĂ©todos baseados em mĂĄquina de vetor de suporteAbstract: Considering the increasing amount of image collections that result from popularization of the digital cameras and the Internet, efficient search methods are becoming increasingly necessary. In this context, this doctoral dissertation proposes new methods for content-based image retrieval based on relevance feedback and on the OPF (optimum-path forest) classifier, being also the first time that the OPF classifier is used in small training sets. This doctoral dissertation names as "greedy" and "planned" the two distinct learning paradigms for relevance feedback taking into account the returned images. The first paradigm attempts to return the images most relevant to the user at each iteration, while the second returns the images considered the most informative or difficult to be classified. The dissertation presents relevance feedback algorithms based on the OPF classifier using both paradigms with single descriptor. Two techniques for combining descriptors are also presented along with the relevance feedback methods based on OPF to improve the effectiveness of the learning process. The first one, MSPS (Multi-Scale Search Parameter), is used for the first time in content-based image retrieval and the second is a consolidated technique based on genetic programming. A new approach of relevance feedback using the OPF classifier at two levels of interest is also shown. In this approach it is possible to select the pixels in images at a level of interest and to choose the most relevant images at each iteration at another level. This dissertation shows that the use of the OPF classifier for content based image retrieval is very efficient and effective, requiring few learning iterations to produce the desired results to the users. Simulations show that the proposed methods outperform the reference methods based on multi-point query and support vector machine. Besides, the methods based on optimum-path forest have shown to be on the average 52 times faster than the SVM-based approachesDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElĂ©tric

    MULTIMEDIA USER PROFILING IN ONLINE SOCIAL NETWORKS

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    Ph.DDOCTOR OF PHILOSOPH

    Combination of Keyword and Visual Features for Web Image Retrieval System

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    Mining photographic collections to enhance the precision and recall of search results using semantically controlled query expansion

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    Driven by a larger and more diverse user-base and datasets, modern Information Retrieval techniques are striving to become contextually-aware in order to provide users with a more satisfactory search experience. While text-only retrieval methods are significantly more accurate and faster to render results than purely visual retrieval methods, these latter provide a rich complementary medium which can be used to obtain relevant and different results from those obtained using text-only retrieval. Moreover, the visual retrieval methods can be used to learn the user’s context and preferences, in particular the user’s relevance feedback, and exploit them to narrow down the search to more accurate results. Despite the overall deficiency in precision of visual retrieval result, the top results are accurate enough to be used for query expansion, when expanded in a controlled manner. The method we propose overcomes the usual pitfalls of visual retrieval: 1. The hardware barrier giving rise to prohibitively slow systems. 2. Results dominated by noise. 3. A significant gap between the low-level features and the semantics of the query. In our thesis, the first barrier is overcome by employing a simple block-based visual features which outperforms a method based on MPEG-7 features specially at early precision (precision of the top results). For the second obstacle, lists from words semantically weighted according to their degree of relation to the original query or to relevance feedback from example images are formed. These lists provide filters through which the confidence in the candidate results is assessed for inclusion in the results. This allows for more reliable Pseudo-Relevance Feedback (PRF). This technique is then used to bridge the third barrier; the semantic gap. It consists of a second step query, re-querying the data set with an query expanded with weighted words obtained from the initial query, and semantically filtered (SF) without human intervention. We developed our PRF-SF method on the IAPR TC-12 benchmark dataset of 20,000 tourist images, obtaining promising results, and tested it on the different and much larger Belga benchmark dataset of approximately 500,000 news images originating from a different source. Our experiments confirmed the potential of the method in improving the overall Mean Average Precision, recall, as well as the level of diversity of the results measured using cluster recall

    Semantic interpretation of events in lifelogging

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    The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment

    DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

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    To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments

    Information retrieval in multimedia databases using relevance feedback algorithms. Applying logistic regression to relevance feedback in image retrieval systems

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    This master tesis deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here, a novel algorithm is presented for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this probability as the output of a linear model whose inputs are the low level image features. The image database is ranked by the output of the model and shown to the user, who selects a few positive and negative samples, repeating the process in an iterative way until he/she is satisfied. The problem of the small sample size with respect to the number of features is solved by adjusting several partial linear models and combining their relevance probabilities by means of an ordered weighted averaged (OWA) operator. Experiments were made with 40 users and they exhibited good performance in finding a target image (4 iterations on average) in a database of about 4700 imagesZuccarello, PD. (2007). Information retrieval in multimedia databases using relevance feedback algorithms. Applying logistic regression to relevance feedback in image retrieval systems. http://hdl.handle.net/10251/12196Archivo delegad

    Proceedings / 17. Workshop Computational Intelligence [Elektronische Ressource] : Dortmund, 5. - 7. Dezember 2007

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    Dieser Tagungsband enthĂ€lt die BeitrĂ€ge des 17. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft fĂŒr Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft fĂŒr Informatik (GI), der vom 5. – 7. Dezember 2007 im Haus Bommerholz bei Dortmund stattfindet. Der GMA-Fachausschuss 5.14 „Computational Intelligence“ entstand 2005 aus den bisherigen FachausschĂŒssen „Neuronale Netze und EvolutionĂ€re Algorithmen“ (FA 5.21) sowie „Fuzzy Control“ (FA 5.22). Der Workshop steht in der Tradition der bisherigen Fuzzy-Workshops, hat aber seinen Fokus in den letzten Jahren schrittweise erweitert. Die Schwerpunkte sind Methoden, Anwendungen und Tools fĂŒr ‱ Fuzzy-Systeme, ‱ KĂŒnstliche Neuronale Netze, ‱ EvolutionĂ€re Algorithmen und ‱ Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen. INHALTSVERZEICHNIS T. Fober, E. HĂŒllermeier, M. Mernberger (Philipps-UniversitĂ€t Marburg): Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules G. Heidemann, S. Klenk (UniversitĂ€t Stuttgart): Visual Analytics for Image Retrieval F. RĂŒgheimer (OvG-UniversitĂ€t Magdeburg): A Condensed Representation for Distributions over Set-Valued Attributes T. Mrziglod (Bayer Technology Services GmbH, Leverkusen): Mit datenbasierten Technologien und Versuchsplanung zu erfolgreichen Produkten H. Schulte (Bosch Rexroth AG, Elchingen): Approximationsgenauigkeit und dynamisches Fehlerwachstum der Modellierung mit Takagi-Sugeno Fuzzy Systemen C. Burghart, R. Mikut, T. Asfour, A. Schmid, F. Kraft, O. Schrempf, H. Holzapfel, R. Stiefelhagen, A. Swerdlow, G. Bretthauer, R. Dillmann (UniversitĂ€t Karlsruhe, Forschungszentrum Karlsruhe GmbH): Kognitive Architekturen fĂŒr humanoide Roboter: Anforderungen, Überblick und Vergleich R. Mikut, C. Burghart, A. Swerdlow (Forschungszentrum Karlsruhe GmbH, UniversitĂ€t Karlsruhe): Ein Gedankenexperiment zum Entwurf einer integrierten kognitiven Architektur fĂŒr humanoide Roboter G. Milighetti, H.-B. Kuntze (FhG IITB Karlsruhe): Diskret-kontinuierliche Regelung und Überwachung von Robotern basierend auf Aktionsprimitiven und Petri-Netzen N. Rosemann, W. Brockmann (UniversitĂ€t OsnabrĂŒck): Kontrolle dynamischer Eigenschaften des Online-Lernens in Neuro-Fuzzy-Systemen mit dem SILKE-Ansatz A. Hans, D. Schneegaß, A. SchĂ€fer, S. Udluft (Siemens AG, TU Ilmenau): Sichere Exploration fĂŒr Reinforcement-Learning-basierte Regelung Th. Bartz-Beielstein, M. Bongards, C. Claes, W. Konen, H. Westenberger (FH Köln): Datenanalyse und Prozessoptimierung fĂŒr Kanalnetze und KlĂ€ranlagen mit CI-Methoden S. Nusser, C. Otte, W. Hauptmann (Siemens AG, OvG-UniversitĂ€t Magdeburg): Learning Binary Classifiers for Applications in Safety-Related Domains W. Jakob, A. Quinte, K.-U. Stucky, W. SĂŒĂŸ, C. Blume (Forschungszentrum Karlsruhe GmbH; FH Köln, Campus Gummersbach) Schnelles Resource Constrained Project Scheduling mit dem EvolutionĂ€ren Algorithmus GLEAM M. Preuß, B. Naujoks (UniversitĂ€t Dortmund): EvolutionĂ€re mehrkriterielle Optimierung bei Anwendungen mit nichtzusammenhĂ€ngenden Pareto-Mengen G. Rudolph, M. Preuß (UniversitĂ€t Dortmund): in mehrkriterielles Evolutionsverfahren zur Bestimmung des Phasengleichgewichts von gemischten FlĂŒssigkeiten Y. Chen, O. Burmeister, C. Bauer, R. Rupp, R. Mikut (UniversitĂ€t Karlsruhe, Forschungszentrum Karlsruhe GmbH, OrthopĂ€dische UniversitĂ€tsklinik Heidelberg): First Steps to Future Applications of Spinal Neural Circuit Models in Neuroprostheses and Humanoid Robots F. Hoffmann, J. Braun, T. Bertram, S. Hölemann (UniversitĂ€t Dortmund, RWTH Aachen): Multikriterielle Optimierung mit modellgestĂŒtzten Evolutionsstrategien S. Piana, S. Engell (UniversitĂ€t Dortmund): EvolutionĂ€re Optimierung des Betriebs von rohrlosen Chemieanlagen T. Runkler (Siemens AG, CT IC 4): Pareto Optimization of the Fuzzy c–Means Clustering Model Using a Multi–Objective Genetic Algorithm H. J. Rommelfanger (J.W. Goethe-UniversitĂ€t Frankfurt am Main): Die Optimierung von Fuzzy-Zielfunktionen in Fuzzy (Mehrziel-) LPSystemen - Ein kritischer Überblick D. Gamrad, D. Söffker (UniversitĂ€t Duisburg-Essen): Formalisierung menschlicher Interaktionen durch Situations-Operator- Modellbildung S. Ritter, P. Bretschneider (FhG AST Ilmenau): Optimale Planung und BetriebsfĂŒhrung der Energieversorgung im liberalisierten Energiemarkt R. Seising (Medizinische UniversitĂ€t Wien): Heinrich Hertz, Ludwig Wittgenstein und die Fuzzy-Strukturen - Eine kleine „Bildergeschichte“ zur Erkenntnisphilosophie J. Limberg, R. Seising (Medizinische UniversitĂ€t Wien): Sequenzvergleiche im Fuzzy-Hypercube M. Steinbrecher, R. Kruse (OvG-UniversitĂ€t Magdeburg): Visualisierung temporaler AbhĂ€ngigkeiten in Bayesschen Netzen M. Schneider, R. Tillmann, U. Lehmann, J. Krone, P. Langbein, U. Stark, J. Schrickel, Ch. Ament, P. Otto (FH SĂŒdwestfalen, Airbus Deutschland GmbH, Hamburg, TU Ilmenau): KĂŒnstliches Neuronales Netz zur Analyse der Geometrie von großflĂ€chig gekrĂŒmmten Bauteilen C. Frey (FhG IITB Karlsruhe): Prozessdiagnose und Monitoring feldbusbasierter Automatisierungsanlagen mittels selbstorganisierender Karte
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