## Distance Learning Networks: learning a similarity-based distance measure for content--based image retrieval

### Bibtex entry :

@article { VG:Squ2000,
author = { David McG. Squire },
title = { Distance Learning Networks: learning a similarity-based distance measure for content--based image retrieval },
journal = { Journal of Visual Communication and Image Representation },
year = { 2000 },
abstract = { In this paper we employ human judgments of image similarity to learn a distance measure for content--based image retrieval. We first derive a statistic, $\kappa_B$, for measuring the agreement between two partitionings of an image set into unlabeled subsets. We then use the results of experiments in which human subjects partition a set of images into unlabeled subsets to define a similarity measure for pairs of images based on the frequency with which they are judged to be similar. We show that, when this measure is used to partition an image set using a clustering technique, the resultant clustering agrees better with those produced by human subjects than any of the feature space-based techniques investigated. Finally, we show that a learning technique based on an extension of a Kohonen network allows a mapping from a numerical feature space to this perceptual similarity space to be learnt which results in partitionings in excellent agreement with those produced by human subjects. },
vgclass = { refpap },
vgproject = { viper },
}
--

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion