Bibliographic description
WARDEN, Ginger; DUNBAR, Denise; WANCZYCKI, Catherine; O'HANLEY, Suanne. The Subject Analysis of Images: Past, Present and Future [on line]. University of British Columbia School of Library, 27th March 2002. Available on:
http://www.slais.ubc.ca/people/students/student-projects/C_Wanczycki/libr517/homepage.html
Dublin Core
Title : The Subject Analysis of Images: Past, Present and Future
Creator : Ginger Warden, Denise Dunbar, Catherine Wanczycki, Suanne O'Hanley
Subject : image collection / image classification / thesaurus / image indexing
Description : "The Art and Architecture Thesaurus (AAT) is a structured vocabulary that can be used to improve access to art, architecture, and material culture."
Publisher : University of British Columbia School of Library
Date : 2002-03-27
Type : Web site
Format : HTML
Identifier : http://www.slais.ubc.ca/people/students/student-projects/C_Wanczycki/libr517/homepage.html
Source: http://www.slais.ubc.ca/
Language : En
Relation : -
Coverage : UK
Rights : No
Extract
"Image collections exist for many purposes: medicine (ultrasounds, CAT scans), architecture (building plans), geography (aerial photos, maps), art (paintings, cartoons), business (trademarks), history (photographs). Some image collections are very large. The Getty Institute's Photo Study Collection, for example, has over two million photographs. Indexing collections of this size can be extremely time consuming, and unlike text, images cannot be searched by keyword. Many automatic indexing systems have been developed, but what computers can currently extract from images are "mostly low-level features" (Rui, 1999) like color, shape, and texture. Research on the information needs of users, and on human perception of images may, in time, contribute the knowledge needed to produce the most precise and efficient retrieval systems possible.
In the meantime, librarians contending with image collections have to make decisions about how best to provide access to them. Currently, there is no universal consensus in libraries. In a survey of 58 libraries in the U.K., (Graham, 1999) the clear majority of respondents employed in-house methods of classifying and indexing their collections, rather than relying on publicized schemes, such as the AAT (Art and Architecture Thesaurus), LCTGM (Library of Congress Thesaurus for Graphic Materials), and LCSH (Library of Congress Subject Headings). This is likely the result of tradition. Curators of image collections were left to their own devices for most of the century, insofar as subject headings for images went, while LCSH concentrated on primarily text-based materials. Many different thesauri were developed by individuals or groups of individuals to deal with particular collections but efforts to create a universally acceptable indexing language for images has only been a point of interest in the past 30 years or so, with the increasing volume of available images and the desire for increased resource-sharing between institutions.
The AAT and LCTGM are presently the two most widely accepted vocabularies for use with image collections. Their development, structure and scope are the main focus of this website. Subject headings from each are applied to several types of images by way of example. We also look to the past and future of subject access to images by surveying both the methods librarians have used in the past (and are still using today to some extent) and the methods that are currently being developed (and to some extent already in place)."
Showing posts with label CLASSIFICATION. Show all posts
Showing posts with label CLASSIFICATION. Show all posts
Thursday, 29 March 2007
Monday, 26 March 2007
Iconclass: iconographic classification system
Bibliographic description
Iconclass: iconographic classification system. The Netherlands Institute for Scientific Information Services, 21 october 2003. Available on: http://www.niwi.knaw.nl/en/geschiedenis/projecten/iconclass/
Dublin Core
Title : Iconclass: iconographic classification system
Creator : ?
Subject : Iconclass / image classification / thesaurus
Description : It's a" classification system for standardized description of the contents of visual documents."
Publisher : the Netherlands Institute for Scientific Information Services (NIWI)
Date : 2003-10-21
Type : article
Format : HTML
Identifier : http://www.niwi.knaw.nl/en/geschiedenis/projecten/iconclass/
Source: http://www.niwi.knaw.nl/nl/
Language : En
Relation : http://www.iconclass.nl/
Coverage : Netherland
Rights : -
Abstract
Iconclass is a classification system for standardized description of the contents of visual documents. [...]
Iconclass is a collection of ready-made classification codes called notations, used to define objects, persons, events, situations, abstract ideas and other potential subjects of visual documents. The approximately 28,000 definitions are arranged in hierarchical order and divided into ten main classes. Some classes are designed for the description of specific subjects, in particular biblical, mythological and literary themes. These are used mainly in art-historical context. Others, containing general subjects, constitute a self-sufficient system offering a place to every subject and activity on earth.
Iconclass: iconographic classification system. The Netherlands Institute for Scientific Information Services, 21 october 2003. Available on: http://www.niwi.knaw.nl/en/geschiedenis/projecten/iconclass/
Dublin Core
Title : Iconclass: iconographic classification system
Creator : ?
Subject : Iconclass / image classification / thesaurus
Description : It's a" classification system for standardized description of the contents of visual documents."
Publisher : the Netherlands Institute for Scientific Information Services (NIWI)
Date : 2003-10-21
Type : article
Format : HTML
Identifier : http://www.niwi.knaw.nl/en/geschiedenis/projecten/iconclass/
Source: http://www.niwi.knaw.nl/nl/
Language : En
Relation : http://www.iconclass.nl/
Coverage : Netherland
Rights : -
Abstract
Iconclass is a classification system for standardized description of the contents of visual documents. [...]
Iconclass is a collection of ready-made classification codes called notations, used to define objects, persons, events, situations, abstract ideas and other potential subjects of visual documents. The approximately 28,000 definitions are arranged in hierarchical order and divided into ten main classes. Some classes are designed for the description of specific subjects, in particular biblical, mythological and literary themes. These are used mainly in art-historical context. Others, containing general subjects, constitute a self-sufficient system offering a place to every subject and activity on earth.
Saturday, 24 March 2007
Photo classification by integrating image content and camera metadata
Bibliographic description
BOUTELL, M., LUO, Jiebo. Photo classification by integrating image content and camera metadata. Rochester University, MN, USA: Department of Computer Science, 23 august 2004. Available on: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1333918
Dublin Core
Title : Photo classification by integrating image content and camera metadata
Creator : Boutell, M. Jiebo Luo
Subject : Image classification / content-based /metadata / semantic classification
Description :
Publisher : IEEE EXPLORE
Date : 2004-08-23
Type : article
Format : PDF
Identifier : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1333918
Source : http://ieeexplore.ieee.org/Xplore/guesthome.jsp
Language : En
Relation : -
Coverage : USA
Rights : Copyright 2006 IEEE
Abstract
Despite years of research, semantic classification of unconstrained photos is still an open problem. Existing systems have only used features derived from the image content. However, Exif metadata recorded by the camera provides cues independent of the scene content that can be exploited to improve classification accuracy. Using the problem of indoor-outdoor classification as an example, analysis of metadata statistics for each class revealed that exposure time, flash use, and subject distance are salient cues. We use a Bayesian network to integrate heterogeneous (content-based and metadata) cues in a robust fashion. Based on extensive experimental results, we make two observations: (1) adding metadata to content-based cues gives highest accuracies; and (2) metadata cues alone can outperform content-based cues alone for certain applications, leading to a system with high performance, yet requiring very little computational overhead. The benefit of incorporating metadata cues can be expected to generalize to other scene classification problems.
BOUTELL, M., LUO, Jiebo. Photo classification by integrating image content and camera metadata. Rochester University, MN, USA: Department of Computer Science, 23 august 2004. Available on: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1333918
Dublin Core
Title : Photo classification by integrating image content and camera metadata
Creator : Boutell, M. Jiebo Luo
Subject : Image classification / content-based /metadata / semantic classification
Description :
Publisher : IEEE EXPLORE
Date : 2004-08-23
Type : article
Format : PDF
Identifier : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1333918
Source : http://ieeexplore.ieee.org/Xplore/guesthome.jsp
Language : En
Relation : -
Coverage : USA
Rights : Copyright 2006 IEEE
Abstract
Despite years of research, semantic classification of unconstrained photos is still an open problem. Existing systems have only used features derived from the image content. However, Exif metadata recorded by the camera provides cues independent of the scene content that can be exploited to improve classification accuracy. Using the problem of indoor-outdoor classification as an example, analysis of metadata statistics for each class revealed that exposure time, flash use, and subject distance are salient cues. We use a Bayesian network to integrate heterogeneous (content-based and metadata) cues in a robust fashion. Based on extensive experimental results, we make two observations: (1) adding metadata to content-based cues gives highest accuracies; and (2) metadata cues alone can outperform content-based cues alone for certain applications, leading to a system with high performance, yet requiring very little computational overhead. The benefit of incorporating metadata cues can be expected to generalize to other scene classification problems.
Sunday, 4 February 2007
Image classification for content-based indexing
Bibliographic description
VAILAYA, Aditya; FIGUEIREDO, Mario A . T; JAIN, Anil K; ZHANG, Hong-Jiang. Image classification for content-based indexing [on line]. IEEE EXPLORE, january 2001. Available on: http://citeseer.ist.psu.edu/correct/686126
Dublin Core
Title : Image classification for content-based indexing
Creator : Aditya Vailaya, Mario A. T. Figueiredo, Anil K. Jain, Hong-Jiang Zhang
Subject : Image classification / Content-based / Indexing
Description : "Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval."
Publisher : IEEE EXPLORE
Date : 2001-01
Type : article
Format : HTML
Identifier : http://citeseer.ist.psu.edu/correct/686126
Source : http://citeseer.ist.psu.edu/
Language : En
Relation : http://citeseer.ist.psu.edu/nrelated/1894793/686126
Coverage : USA
Rights : Copyright 2001 IEEE
Extract
"Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier."
VAILAYA, Aditya; FIGUEIREDO, Mario A . T; JAIN, Anil K; ZHANG, Hong-Jiang. Image classification for content-based indexing [on line]. IEEE EXPLORE, january 2001. Available on: http://citeseer.ist.psu.edu/correct/686126
Dublin Core
Title : Image classification for content-based indexing
Creator : Aditya Vailaya, Mario A. T. Figueiredo, Anil K. Jain, Hong-Jiang Zhang
Subject : Image classification / Content-based / Indexing
Description : "Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval."
Publisher : IEEE EXPLORE
Date : 2001-01
Type : article
Format : HTML
Identifier : http://citeseer.ist.psu.edu/correct/686126
Source : http://citeseer.ist.psu.edu/
Language : En
Relation : http://citeseer.ist.psu.edu/nrelated/1894793/686126
Coverage : USA
Rights : Copyright 2001 IEEE
Extract
"Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier."
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