Fast Searching by Content in Multimedia Databases.

@article{DBLP:journals/debu/Faloutsos95,
  author    = {Christos Faloutsos},
  title     = {Fast Searching by Content in Multimedia Databases},
  journal   = {IEEE Data Eng. Bull.},
  volume    = {18},
  number    = {4},
  year      = {1995},
  pages     = {31-40},
  ee        = {db/journals/debu/Faloutsos95.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We describe a domain-independent method to search multimedia databases by content. Examples of these searches include 'find all images that look like this graphic drawing (which is a photograph of a sunset)' in a collection of color images; 'find stocks that move like Motorola's' in a collection of stock price movements; and 'find patterns with stripes containing red and white' in a collection of retail catalog items.

In all these applications, we assume that there exists a distance function, which measures the dis-similarity between two objects. Given that, the idea is to extract f numerical features from each object, effectively mapping it into a point in f-dimensional space. Subsequently, any spatial access method (like the R-trees) can be used to search for similar objects (that is, nearby points in the f-d space). Comparing the features corresponds to a 'quick and dirty' test, which will help us exclude a large number of non-qualifying objects. The test could allow for false alarms, but no false dismissals. This implies that the mapping from objects to f-d points should preserve the distance, or, as we show, it should lower-bound it.

We briefly show how this idea can be applied to achieve fast searching for time sequences and for color images. Experiments on real or realistic databases show that it is much faster than sequential scanning, while not missing any qualifying objects, as expected from the lower-bounding lemma. Thus, this approach can be used for any database of multimedia objects, as long as the lower-bounding lemma is satisfied.

Copyright © 1995 by the author(s). Abstract used with permission.

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Referenced by

1. Norbert Fuhr: Models for Integrated Information Retrieval and Database Systems. IEEE Data Eng. Bull. 19(1): 3-13(1996)