Local Similarity Search for Content-based Image Retrieval
This event took place on Wednesday 29 November 2006 at 11:30
Peter Howarth Imperial College London, and KMi, The Open University
The goal of content-based image retrieval (CBIR) is to provide the user with a way to browse or retrieve images from large collections based on visual similarity. At the heart of any CBIR system are visual features that have been extracted from images and (dis)similarity functions that are used to quantify the similarity between these features. The combination of these two components will drive the overall performance of a system.
Two frequently studied research areas in CBIR are maximising retrieval performance using similarity measures and improving the efficiency and speed of search by applying indexing methods. Often these are mutually exclusive. The best performing similarity measures are usually computationally expensive and the optimal indexing approaches can place many restrictions on what features and similarity functions can be used.
In this talk we investigate how to localise the measurement of similarity. That is, emphasise points that are close to the query in some subspace of the full feature space. We show that this has dual benefits for CBIR, both improving retrieval performance and speeding up the search of high-dimensional features.
This event took place on Wednesday 29 November 2006 at 11:30
The goal of content-based image retrieval (CBIR) is to provide the user with a way to browse or retrieve images from large collections based on visual similarity. At the heart of any CBIR system are visual features that have been extracted from images and (dis)similarity functions that are used to quantify the similarity between these features. The combination of these two components will drive the overall performance of a system.
Two frequently studied research areas in CBIR are maximising retrieval performance using similarity measures and improving the efficiency and speed of search by applying indexing methods. Often these are mutually exclusive. The best performing similarity measures are usually computationally expensive and the optimal indexing approaches can place many restrictions on what features and similarity functions can be used.
In this talk we investigate how to localise the measurement of similarity. That is, emphasise points that are close to the query in some subspace of the full feature space. We show that this has dual benefits for CBIR, both improving retrieval performance and speeding up the search of high-dimensional features.
Future Internet
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Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
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