## Learning a similarity-based distance measure for image database organizationfrom human partitionings of an image set

### Bibtex entry :

@inproceedings { VG:Squ1998b,
author = { David McG. Squire },
title = { Learning a similarity-based distance measure for image database organizationfrom human partitionings of an image set },
booktitle = { Fourth IEEE Workshop on Applications of Computer Vision (WACV'98) },
pages = { 88--93 },
year = { 1998 },
address = { Princeton, NJ, USA },
month = { October },
url = { http://vision.unige.ch/publications/postscript/98/Squire_wacv98.ps.gz },
abstract = { In this paper we employ human judgments of image similarity to improvethe organization of an image database. We first derive a statistic,$\kappa_B$ which measures the agreement between two partitioningsof an image set. $\kappa_B$ is used to assess agreement both amongstand between human and machine partitionings. This provides a rigorousmeans of choosing between competing image database organization systems,and of assessing the performance of such systems with respect tohuman judgments. Human partitionings of an image set are used todefine an similarity value based on the frequency with which imagesare judged to be similar. When this measure is used to partitionan image set using a clustering technique, the resultant partitioningagrees better with human partitionings than any of the feature-space-basedtechniques investigated. Finally, we investigate the use of multilayerperceptrons and a \emph{Distance Learning Network} to learn a mappingfrom feature space to this perceptual similarity space. The DistanceLearning Network is shown to learn a mapping which results in partitioningsin excellent agreement with those produced by human subjects. },
owner = { steph },
timestamp = { 2008.05.04 },
url1 = { http://vision.unige.ch/publications/postscript/98/Squire_wacv98.pdf },
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