## A Comparison of Human and Machine Assessments of Image Similarityfor the Organization of Image Databases

### Bibtex entry :

@inproceedings { VG:SqP1997,
author = { David McG. Squire and Thierry Pun },
title = { A Comparison of Human and Machine Assessments of Image Similarityfor the Organization of Image Databases },
booktitle = { The 10th Scandinavian Conference on Image Analysis },
pages = { 51--58 },
year = { 1997 },
editor = { Michael Frydrych and Jussi Parkkinen and Ari Visa },
address = { Lappeenranta, Finland },
month = { June },
keywords = { image similarity, image database organization, agreement statistics,VG:SqP1997key },
url = { http://vision.unige.ch/publications/postscript/97/SquirePun_scia97.ps.gz },
abstract = { There has recently been a significant interest in the organizationand \emph{content-based} querying of large images databases. Mostfrequently, the underlying hypothesis is that image similarity canbe characterized by low-level image features, without further abstraction.This assumes that there is sufficient agreement between machine andhuman measures of image similarity for the database to be useful.We wish to assess the veracity of this assumption. To this end, wedevelop measures of the agreement between two partitionings of animage set; we show that it is vital to take chance agreements intoaccount. We then use these measures to assess the agreement betweenhuman subjects and a variety of machine clustering techniques ona set of images. The results can be used to select and refine imagedistance measures for querying and organizing image databases. },
owner = { steph },
timestamp = { 2008.05.04 },
url1 = { http://vision.unige.ch/publications/postscript/97/SquirePun_scia97.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