Indexing and retrieval scheme of the image database based on color and spatial relations

[[abstract]]We propose a new approach to retrieve images from an image database. We combine both color and spatial features of a picture to index and measure the similarity of images. We propose a new automatic indexing scheme of the image database according to our clustering method which could filter the image efficiently. As a technical contribution, a Seed-Filling like algorithm that could extract the shape and spatial relationship feature of the image is proposed. Also, the system is incorporated with a visual interface, which allows the user to specify objects as the spatial specification of pictures. With color identification and spatial similarity functions, the preliminary experience shows that the system is able to retrieve image information of a very high satisfaction.[[conferencetype]]國際[[conferencedate]]20000730~20000802[[booktype]]紙本[[conferencelocation]]New York, NY, US

[[alternative]]A Flexible Content-based Image Retrieval System Integrating with Color, Shape and Spatial Relations

計畫編號：NSC89-2218-E032-013研究期間：200008~200107研究經費：856,000[[sponsorship]]行政院國家科學委員

Modeling image databases using Xml schema

This thesis presents a model for still images in order to support content-based querying and browsing by hierarchical tree structures and object relational graphs. We use the extensible markup language (XML) schema to illustrate and exemplify the proposed model because of its interoperability and flexibility advantages. Of primary interest is the notion of complex types and referential integrity to fully describe the physical and semantic properties of images. XQuery is used to support query processing. We further show how these complex types of XML schema can be used to overcome the shortcomings of reported image database descriptions in the literature

Ανάκτηση εικόνας με βάση το περιεχόμενο, Το πρότυπο MPEG-7. Μελέτη περιπτώσεων: Alipr.com

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2010.Στην παρούσα διπλωματική εργασία γίνεται μια ανασκόπηση της βιβλιογραφίας σχετικά με την ανάκτηση της εικόνας από το ξεκίνημα των συστημάτων ανάκτησης μέχρι σήμερα. Πλέον η πληροφορία που διανέμεται μέσω του διαδικτύου είναι τεράστια και ένα μεγάλο μέρος της εργασίας αναλύει τα μέσα τα οποία υπάρχουν για την ταξινόμηση της πολυμεσικής πληροφορίας και συγκεκριμένα της εικόνας. Επίσης γίνεται μια παρουσίαση του γενικού προτύπου για τα πολυμέσα MPEG-7 και μελετούνται κάποιες περιπτώσεις ιστοσελίδων μηχανών αναζήτησης εικόνων

Les mixtures de Dirichlet et leurs apports pour la classification et la recherche d'images par le contenu

Le développement de la médecine moderne dans le domaine des techniques de diagnostic comme la radiologie, l'histopathologie et la tomographie avait comme résultat l'explosion du nombre et de l'importance des images médicales sauvegardées par la majorité des hôpitaux. Afin d'aider les médecins à confirmer leurs diagnostics, plusieurs systèmes de recherche d'images médicales ont vu le jour. La conception de ces systèmes présente plusieurs étapes. Nous pensons que le résumé des bases de données d'images est une étape importante dans chaque système de recherche. En effet, la catégorisation d'une base de données d'images facilite énormément la recherche et permet de localiser les images voulues en un minimum de temps. Dans ce mémoire, nous étudions en un premier temps, les différents problèmes communs à tous les systèmes de recherche d'images à savoir l'indexation, l'extraction des caractéristiques, la définition des mesures de similarités et le retour de pertinence. Nous étudions aussi d'autres catégories de problèmes spécifiques à la recherche d'images. Cette étude est complétée par une analyse des systèmes existants les plus connus. Dans la deuxième partie du mémoire, nous nous intéressons aux mixtures de Dirichlet et comment on peut les exploiter pour la classification, en particulier le résumé des bases de données d'images. Contrairement aux approches classiques qui considèrent la loi normale comme densité, nous utilisons une généralisation de la Dirichlet pour l'adapter plus aux problèmes réels. Notre approche est traduite par un modèle mathématique basé sur le maximum de vraisemblance et la méthode de Fisher. Une interprétation très intéressante de notre méthode, basée sur la statistique géométrique, est donnée. Finalement, nous présentons des évaluations contextuelles et non-contextuelles, qui prouvent la validité de notre méthode

Attribute-based Image Retrieval: Towards Bridging the Semantic and Intention Gaps

Ph.DDOCTOR OF PHILOSOPH

Beyond Visual Words: Exploring Higher - Level Image Representation For Object Categorization

Ph.DDOCTOR OF PHILOSOPH

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

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

Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

Proceedings of the Third Edition of the Annual Conference on Wireless On-demand Network Systems and Services (WONS 2006)

Ce fichier regroupe en un seul documents l'ensemble des articles accéptés pour la conférences WONS2006/http://citi.insa-lyon.fr/wons2006/index.htmlThis year, 56 papers were submitted. From the Open Call submissions we accepted 16 papers as full papers (up to 12 pages) and 8 papers as short papers (up to 6 pages). All the accepted papers will be presented orally in the Workshop sessions. More precisely, the selected papers have been organized in 7 session: Channel access and scheduling, Energy-aware Protocols, QoS in Mobile Ad-Hoc networks, Multihop Performance Issues, Wireless Internet, Applications and finally Security Issues. The papers (and authors) come from all parts of the world, confirming the international stature of this Workshop. The majority of the contributions are from Europe (France, Germany, Greece, Italy, Netherlands, Norway, Switzerland, UK). However, a significant number is from Australia, Brazil, Canada, Iran, Korea and USA. The proceedings also include two invited papers. We take this opportunity to thank all the authors who submitted their papers to WONS 2006. You helped make this event again a success