Toward complete performance characterization in content-based retrieval

Nicu Sebe; Nies Huijsmans; Qi Tian; Theo Gevers

doi:10.1117/12.586762

17 January 2005 Toward complete performance characterization in content-based retrieval

Nicu Sebe, Nies Huijsmans, Qi Tian, Theo Gevers

Proceedings Volume 5670, Internet Imaging VI; (2005) https://doi.org/10.1117/12.586762
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

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.

Citation Download Citation

Nicu Sebe, Nies Huijsmans, Qi Tian, and Theo Gevers "Toward complete performance characterization in content-based retrieval", Proc. SPIE 5670, Internet Imaging VI, (17 January 2005); https://doi.org/10.1117/12.586762

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available