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Ranking method for optimizing precision/recall of content-based image retrieval

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conference contribution
posted on 2009-01-01, 00:00 authored by Jun Zhang, L Ye
The ranking method is a key element of Content-based Image Retrieval (CBIR) system, which can affect the final retrieval performance. In the literature, previous ranking methods based on either distance or probability do not explicitly relate to precision and recall, which are normally used to evaluate the performance of CBIR systems. In this paper, a novel ranking method based on relative density is proposed to improve the probability based approach by ranking images in the class. The proposed method can achieve optimal precision and recall. The experiments conducted on a large photographic collection show significant improvements of retrieval performance.

History

Event

Ubiquitous, Autonomic and Trusted Computing. Conference (2009 : Brisbane, Qld.)

Pagination

356 - 361

Publisher

Institute of Electrical and Electronics Engineers

Location

Brisbane, Qld.

Place of publication

Piscataway, N. J.

Start date

2009-07-07

End date

2009-07-09

ISBN-13

9780769537375

Language

eng

Notes

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2009, IEEE

Editor/Contributor(s)

B Werner

Title of proceedings

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with The UIC2009 and ATC 2009 Conferences

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