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Proceedings Paper

Toward complete performance characterization in content-based retrieval
Author(s): Nicu Sebe; Nies Huijsmans; Qi Tian; Theo Gevers
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Paper Abstract

In Content-based Image Retrieval the comparison of a query image and each of the database images is defined by a similarity distance obtained from the two feature vectors involved. These feature vectors can be seen as sets of noisy indexes. Unlike text matching (that is exact) image matching is only approximate, leading to ranking methods. Only images at the top ranks (within the scope) are returned as retrieval results. Image retrieval performance characterization has mainly been based on measures available from probabilistic text retrieval in the form of Precision-Recall or Precision-Scope graphs. However, these graphs offer an incomplete overview of the image retrieval system under study. Essential information about how the success of the query is influenced by the size and type of irrelevant images is missing. Due to the inexactness of the visual matching process, the effect of the irrelevant embedding, represented in the additional performance measure generality, plays an important role. In general, a performance graph will be three-dimensional, a Generality-Recall-Precision Graph. By choosing appropriate scope values a new two-dimensional performance graph, the Generality-Recall-Precision Graph, is proposed to replace the commonly used Precision-Recall Graph, as the better choice for total recall studies.

Paper Details

Date Published: 17 January 2005
PDF: 12 pages
Proc. SPIE 5670, Internet Imaging VI, (17 January 2005); doi: 10.1117/12.586762
Show Author Affiliations
Nicu Sebe, Univ. van Amsterdam (Netherlands)
Nies Huijsmans, Leiden Univ. (Netherlands)
Qi Tian, Univ. of Texas/San Antonio (United States)
Theo Gevers, Univ. van Amsterdam (Netherlands)

Published in SPIE Proceedings Vol. 5670:
Internet Imaging VI
Simone Santini; Raimondo Schettini; Theo Gevers, Editor(s)

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