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Relevance Feedback Models for Content-Based Image Peter Auer and Alex Po Leung
 

Summary: Relevance Feedback Models for Content-Based Image
Retrieval
Peter Auer and Alex Po Leung
Department Mathematik und Informationstechnologie,
Montanuniversit¨at at Leoben
Franz-Josef-Straße 18, 8700-Leoben, Austria
{auer,alex.leung}@unileoben.ac.at
Abstract. We investigate models for content-based image retrieval with relevance
feedback, in particular focusing on the exploration-exploitation dilemma. We pro-
pose quantitative models for the user behavior and investigate implications of these
models. Three search algorithms for efficient searches based on the user models are
proposed and evaluated. In the first model a user queries a database for the most (or
a sufficiently) relevant image. The user gives feedback to the system by selecting
the most relevant image from a number of images presented by the system. In the
second model we consider a filtering task where relevant images should be extracted
from a database and presented to the user. The feedback of the user is a binary clas-
sification of each presented image as relevant or irrelevant. While these models are
related, they differ significantly in the kind of feedback provided by the user. This
requires very different mechanisms to trade off exploration (finding out what the
user wants) and exploitation (serving images which the system believes relevant for

  

Source: Auer, Peter - Department Mathematik und Informationstechnologie, Montanuniversität Leoben

 

Collections: Computer Technologies and Information Sciences