%0 Journal Article
	%A Mawloud Mosbah and  Bachir Boucheham
	%D 2014
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 88, 2014
	%T Relevance Feedback within CBIR Systems
	%U https://publications.waset.org/pdf/9997960
	%V 88
	%X We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.

	%P 568 - 572