Adaptive Multimedia Retrieval:User, Context, and Feedback
Description
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.
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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.
