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

2007

@article{GoH2007,
	vgclass =	{refpap},
	author =	{Iker Gondra and Douglas R. Heisterkamp},
	title =	{Content-based image retrieval with the normalized
	information distance},
	journal =	{Computer Vision and Image Understanding},
	year =	{2007},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.cviu.2007.11.001},
	abstract =	{The main idea of content-based image retrieval (CBIR) is
	to search on an image's visual content directly. Typically, features
	(e.g., color, shape, texture) are extracted from each image and
	organized into a feature vector. Retrieval is performed by image
	example where a query image is given as input by the user and an
	appropriate metric is used to find the best matches in the
	corresponding feature space. We attempt to bypass the feature selection
	step (and the metric in the corresponding feature space) by following
	what we believe is the logical continuation of the CBIR idea of
	searching visual content directly. It is based on the observation that,
	since ultimately, the entire visual content of an image is encoded into
	its raw data (i.e., the raw pixel values), in theory, it should be
	possible to determine image similarity based on the raw data alone. The
	main advantage of this approach is its simplicity in that explicit
	selection, extraction, and weighting of features is not needed. This
	work is an investigation into an image dissimilarity measure following
	from the theoretical foundation of the recently proposed normalized
	information distance (NID) [M. Li, X. Chen, X. Li, B. Ma, P. Vitanyi,
	The similarity metric, in: Proceedings of the 14th ACM-SIAM Symposium
	on Discrete Algorithms, 2003, pp. 863-872]. Approximations of the
	Kolmogorov complexity of an image are created by using different
	compression methods. Using those approximations, the NID between images
	is calculated and used as a metric for CBIR. The compression-based
	approximations to Kolmogorov complexity are shown to be valid by
	proving that they create statistically significant dissimilarity
	measures by testing them against a null hypothesis of random retrieval.
	Furthermore, when compared against several feature-based methods, the
	NID approach performed surprisingly well.},
}