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Is similarity search useful for high dimensional spaces?
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| Title |
Is similarity search useful for high dimensional spaces? |
| Author(s) |
R. Weber, P. Zezula |
| Type |
misc |
| Booktitle |
In International Workshop on Similarity Search (IWOSS'99), Florence, Italy |
| Organization |
Institute for Information Systems, ETH Zurich |
| Month |
September
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| Year |
1999 |
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Abstract
In recent years, multimedia content-based retrieval has become an
important research problem. In order to provide effective and also
efficient access to relevant data stored in large (often distributed)
digital repositories, advanced software tools are necessary.
Content-based retrieval works on the idea of abstracting the contents
of an object, for example color or shape in the case of images, by
so-called features - features are typically points in a
high-dimensional vector space. Instead of determining the similarity
of two objects based on their raw data, only the much smaller feature
representations are used to estimate the objects' similarity. Given a
reference (query) object represented by its features, similarity
predicates are defined to retrieve a specific number of best cases or
all objects satisfying a (distance) constraint. In this respect, we
can distinguish between similarity range and nearest neighbor (NN)
queries.
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