Paper Info
Title
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap.
Abstract
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, some memory is incorporated in the procedure by weighting the current and previous scores. Also, a novel evaluation procedure is proposed in this work based on the empirical commutative distribution functions of the relevant and non-relevant retrieved images. Good experimental results are achieved in very different experimental setups and tested in different databases.
Year
DOI
Venue
2015
10.1016/j.neucom.2015.05.041
Neurocomputing
Keywords
Field
DocType
Semantic gap,Proportional odds model,Information retrieval,Relevance feedback,Content-based image retrieval
Feature vector,Weighting,Conditional probability distribution,Dimensionality reduction,Pattern recognition,Semantic gap,Image retrieval,Artificial intelligence,Probability density function,Mathematics,Content-based image retrieval,Machine learning
Journal
Volume
ISSN
Citations 
168
0925-2312
1
PageRank 
References 
Authors
0.35
17
5
Name
Order
Citations
PageRank
Esther De Ves1708.61
Guillermo Ayala29516.13
Xaro Benavent311212.31
Juan Domingo43319258.54
Esther Dura583.60