Refereed full papers (journals, book chapters, international conferences)

2004

@article{STH2004,
	vgclass =	{refpap},
	author =	{Zoran Steji\'{c} and Yasufumi Takama and Kaoru Hirota},
	title =	{Mathematical aggregation operators in image retrieval:
	effect on retrieval performance and role in relevance feedback},
	journal =	{Signal Processing},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.sigpro.2004.10.003},
	abstract =	{We examine the effect of mathematical aggregation
	operators on the image retrieval performance, by empirically comparing
	67 operators, applied to the problem of computing the overall image
	similarity, given a collection of individual feature similarities.
	While most of the existing image similarity models express the overall
	image similarity as an aggregation of multiple feature similarities, no
	study presents a comprehensive comparison of the different operators.
	For the comparison, we use a diverse test collection with around 2500
	images in 62 semantic categories. Results show that the retrieval
	performance strongly depends on the mathematical aggregation
	operator(s) employed within the image similarity model--the difference
	in the average retrieval precision between the best performing and the
	worst performing of the 67 operators is over 40\%. Based on this
	observation, we propose a genetic algorithm-based relevance feedback
	technique--called Local Aggregation Pattern (LAP)--which adapts the
	image similarity model to the user by modifying the combination of
	aggregation operators employed within the model to aggregate multiple
	feature similarities into the overall image similarity. Evaluated on
	the 2500 images test collection, the proposed LAP technique is shown to
	outperform the existing relevance feedback techniques--by over 5\%
	higher average retrieval precision. Furthermore, by modifying the
	combination of aggregation operators rather than the relevance of image
	features, the proposed LAP technique is complementary to the majority
	of the existing relevance feedback techniques, with which it can be
	naturally coupled to further improve the image retrieval performance.},
}