Authors:
Hanen Karamti
;
Mohamed Tmar
and
Faiez Gargouri
Affiliation:
University of Sfax, Tunisia
Keyword(s):
CBIR, Image, Query, Relevance Feedback, Rocchio, Neural Network.
Related
Ontology
Subjects/Areas/Topics:
Multimedia and User Interfaces
;
Searching and Browsing
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
In the Content-based image retrieval (CBIR) system, user can express his interest with an image to search
images from large database. The retrieval technique uses only the visual contents of images. In recent years
with the technological advances, there remain many challenging research problems that continue to attract
researchers from multiple disciplines such as the indexing, storing and browsing in the large database. However,
traditional methods of image retrieval might not be sufficiently effective when dealing these research
problems. Therefore there is a need for an efficient way for facilitate to user to find his need in these large
collections of images. Therefore, building a new system to retrieve images using the relevance feedback’s
technique is necessary in order to deal with such problem of image retrieval.
In this paper, a new CBIR system is proposed to retrieve the similar images by integrating a relevance feedback.
This system can be exploited to discover a new prope
r query representation and to improve the relevance
of the retrieved results. The results obtained by our system are illustrated through some experiments on images
from the MediaEval2014 collection.
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