## Assessing Agreement Between Human and Machine Clusterings of Image Databases

### Bibtex entry :

@techreport { VG:SqP1997a,
author = { David McG. Squire and Thierry Pun },
title = { Assessing Agreement Between Human and Machine Clusterings of Image Databases },
institution = { Computer Vision Group, Computing Centre, University of Geneva },
year = { 1997 },
number = { 97.03 },
address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\eve, Switzerland },
month = { April },
url = { http://vision.unige.ch/publications/postscript/97/VGTR97.03_SquirePun.ps.gz },
abstract = { There is currently much interest in the organization and \emph{content-based} querying image databases. The usual hypothesis is that image similarity can be characterized by low-level features, without further abstraction. This assumes that agreement between machine and human measures of similarity is sufficient for the database to be useful. To assess this assumption, we develop measures of the agreement between partitionings of an image set, showing that chance agreements \emph{must} be considered. These measures are used to assess the agreement between human subjects and several machine clustering techniques on an image set. The results can be used to select and refine distance measures for querying and organizing image databases. },
url1 = { http://vision.unige.ch/publications/postscript/97/VGTR97.03_SquirePun.pdf },
vgclass = { report },
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