PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Query Refinement Suggestion in Multimodal Image Retrieval with Relevance Feedback
Luis A. Leiva, Mauricio Villegas and Roberto Paredes
In: ICMI 2011(2011).

Abstract

In the literature, it has been shown that relevance feedback is a good strategy for the system to interact with the user and provide better results in a content-based image retrieval (CBIR) system. On the other hand, there are many re- trieval systems which suggest a refinement of the query as the user types, which effectively helps the user to obtain better results with less effort. Based on these observations, in this work we propose to add a suggested query refine- ment as a complement in an image retrieval system with relevance feedback. Taking advantage of the nature of the relevance feedback, in which the user selects relevant im- ages, the query suggestions are derived using this relevance information. From the results of an evaluation performed, it can be said that this type of query suggestion is a very good enhancement to the relevance feedback scheme, and can potentially lead to better retrieval performance and less effort from the user.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:8782
Deposited By:Alfons Juan
Deposited On:21 February 2012