Marchand-Maillet / Bruno / Nürnberger Adaptive Multimedia Retrieval:User, Context, and Feedback

4th International Workshop, AMR 2006, Geneva, Switzerland, July, 27-28, 2006, Revised Selected Papers
2007
ISBN: 978-3-540-71545-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 4398, 281 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006.

The 18 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement. Also included are two invited contributions that have been intended to open on less-addressed topics in the community, as it is the case for music information retrieval and distributed information retrieval. The papers are organized in topical sections on ontology-based retrieval and annotation, ranking and similarity measurements, music information retrieval, visual modelling, adaptive retrieval, structuring multimedia, as well as user integration and profiling.

Written for: Researchers and professionals

Keywords: Web services, adaptive multimedia retrieval, image retrieval, machine learning, multimedia databases, multimedia information retrieval, musical content, musical similarity, personalization, retrieval systems, segmentation, semantic classification, spatio-temporal relations, user interfaces, user profiling, video retrieval, visual modeling.

Zielgruppe

Research

Autoren/Hrsg.

Marchand-Maillet, Stéphane

Marchand-Maillet, Stephane

Bruno, Eric

Nürnberger, Andreas

Detyniecki, Marcin

Inhaltsverzeichnis

Ontology-Based Retrieval and Annotation.- A Method for Processing the Natural Language Query in Ontology-Based Image Retrieval System.- SAFIRE: Towards Standardized Semantic Rich Image Annotation.- Ontology-Supported Video Modeling and Retrieval.- Ranking and Similarity Measurements.- Learning to Retrieve Images from Text Queries with a Discriminative Model.- A General Principled Method for Image Similarity Validation.- Rank-Test Similarity Measure Between Video Segments for Local Descriptors.- Music Information Retrieval.- Can Humans Benefit from Music Information Retrieval?.- Visual Modelling.- A New Approach to Probabilistic Image Modeling with Multidimensional Hidden Markov Models.- 3D Face Recognition by Modeling the Arrangement of Concave and Convex Regions.- Fuzzy Semantic Action and Color Characterization of Animation Movies in the Video Indexing Task Context.- Retrieval of Document Images Based on Page Layout Similarity.- Adaptive Retrieval.- Multimedia Content Adaptation Within the CAIN Framework Via Constraints Satisfaction and Optimization.- Aspects of Adaptivity in P2P Information Retrieval.- Interactive Museum Guide: Accurate Retrieval of Object Descriptions.- Structuring Multimedia.- Semantic Image Retrieval Using Region-Based Relevance Feedback.- Image Retrieval with Segmentation-Based Query.- Fast Structuring of Large Television Streams Using Program Guides.- User Integration and Profiling.- Variation of Relevance Assessments for Medical Image Retrieval.- An Efficient Collaborative Information Retrieval System by Incorporating the User Profile.- The Potential of User Feedback Through the Iterative Refining of Queries in an Image Retrieval System.

Leseproben

STRONG>1 Introduction (p. 11)
Nowadays, the study on the image retrieval has been actively progressing. Until now, the basic image retrieval methodologies are the Text-Matching, Contents-based and Concept(Ontology)-based methods.[2][3] In these methodologies, users generally use simple keywords as the user query. The Ontology-based image retrieval system uses the ontologies to understand the meaning of the user query, but the ontologies just solve the ambiguousness between words. Hence, the user query used in ontologybased system is also simple keywords. Nowadays, huge number of images has been creating through the various image acquisition devices such as the digital camera, scanner and phone-camera.

Thus, we need more intelligent image retrieval techniques for searching the images efficiently. In present day, the users tend to use a descriptive sentence to find images because they want to search for images as fast as possible, they do not want to spend long time retrieving images. Thus, the user query is getting descriptive and natural language type. As a result, the method for processing the natural language query is demanded for improving the performance of the image retrieval system. In this paper, we use two kinds of ontologies in our proposed system to handle the natural language query.

One is the domain ontology, which contains many concepts and represents the relations between these concepts. The other is the spatial ontology, which contains three basic relations and many words about the relations. We use some parts of the WordNet for building the domain ontology and we newly make the spatial ontology based on the survey paper, WordNet and OXFORD Dictionary for the purpose of processing the natural language queries. The basic idea of our study is that most user queries are including the words representing the spatial relationships. It is the significant feature of user queries for supporting our study.

Therefore we use the features to design the newly proposed image retrieval system and try to process the natural language queries. In the 2nd Section, we introduce the related works - the ontology-based image retrieval and the query processing methodologies. Then in Section 3, we explain the spatial ontology building steps and our system architecture based on the ontologies. And we describe the method for processing the natural language queries in the ontology- based system in details. We test and evaluate our system comparing with other systems in Section 4. At the end of this paper, we conclude our study and suggest the future works.

2 Related Works

2.1 Ontology-Based Image Retrieval
The traditional information retrieval systems have the mismatch problem among the terminologies. For solving the problem, many researchers have studied to apply the ontology theory to the system. Many works show that ontologies could be used not only for annotation and precise information retrieval, but also for helping the user in formulating the information need and the corresponding query.

It is important especially in applications where the domain semantics are complicated and not necessarily known to the user. Furthermore, the ontology-enriched knowledge base of image metadata can be applied to construct more meaningful answers to queries than just hit-lists. The major difficulty in the ontology-based approach is that the extra work is needed in creating the ontology and the detailed annotations.