DocumentCode :
2298690
Title :
Ranking Method for Optimizing Precision/Recall of Content-Based Image Retrieval
Author :
Zhang, Jun ; Ye, Lei
Author_Institution :
Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia
fYear :
2009
fDate :
7-9 July 2009
Firstpage :
356
Lastpage :
361
Abstract :
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.
Keywords :
content-based retrieval; image retrieval; optimisation; CBIR systems; content-based image retrieval; large photographic collection; optimal precision; optimal recall; optimization; ranking method; Australia; Computer science; Conferences; Content based retrieval; Image retrieval; Image sequences; Information retrieval; Optimization methods; Pervasive computing; Software engineering; Content-based image retrieval; performance evaluation; ranking method;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Ubiquitous, Autonomic and Trusted Computing, 2009. UIC-ATC '09. Symposia and Workshops on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4244-4902-6
Electronic_ISBN :
978-0-7695-3737-5
Type :
conf
DOI :
10.1109/UIC-ATC.2009.9
Filename :
5319211
Link To Document :
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