Authors:
Luca Piras
;
Deiv Furcas
and
Giorgio Giacinto
Affiliation:
University of Cagliari, Italy
Keyword(s):
Feature Space Exploration, Nearest Neighbour, Relevance Feedback, Query Shifting, Image Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Pattern Recognition
;
Similarity and Distance Learning
;
Theory and Methods
Abstract:
Learning what a specific user is exactly looking for, during a session of image search and retrieval, is a problem
that has been mainly approached with “classification” or “exploration” techniques. Classification techniques
follow the assumption that the images in the archive are statically subdivided into classes. Exploration approaches,
on the other hand, are more focused on following the varying needs of the user. It turns out that
image retrieval techniques based on classification approaches, though often showing good performances, are
not prone to adapt to different users’ goals. In this paper we propose a relevance feedback mechanism that
drives the search into promising regions of the feature space according to the Nearest Neighbor paradigm. In
particular, each image labelled as being relevant by the user, is used as a “seed” for an exploration of the space
based on the Nearest Neighbors paradigm. Reported results show that this technique allows attaining higher
recall and ave
rage precision performances than other state-of-the-art relevance feedback approaches.
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