A Content-Based Image Retrieval Service for Archaeology Collections

Vemuri, Naga Srinivas; Torres, Ricardo da Silva; Shen, Rao; Gonçalves, Marcos André; Fan, Weiguo; Fox, Edward A.

doi:10.1007/11863878_37

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…While promising results have been seen in some studies (Barnard, Duygulu, & Forsyth, 2001;Vemuri et al, 2006;Wang, Li, & Lin, 2003;Wang, Li, & Wiederhold, 2001;Ward et al, 2001) retrieval methods that rely on visual features alone seem at the moment to be a useful analytic tool (Vemuri et al, 2006), rather than one that can support information retrieval across large numbers of works.…”

Section: Content [Image] Based Retrievalmentioning

confidence: 99%

Social Classification and Folksonomy in Art Museums: Early Data From the STEVE.MUSEUM Tagger Prototype

Trant¹

2006

Advances in Classification Research Online

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The collections of art museums have been assembled over hundreds of years and described, organized and classified according to traditions of art historical research and discourse. Art museums, in their role as curators and interpreters of the cultural record, have developed standards for the description of works of art (such as the Categories for the Description of Works of Art, CDWA) that emphasize the physical nature of art as artefact, the authorial role of the creator, the temporal and cultural context of creation and ownership, and the scholarly significance of the work over time. Collections managers have recorded conservation, exhibition, loan and publication history, along with significant volumes of internal documentation of acquisition and storage, that support the custody and care of artefacts of significant cultural value. But the systems of documentation and classification that support the professional discourse of art history and the management of museum collections have failed to represent the interests, perspectives or passions of those who visit [use?] museum collections, both on-site and online. As museums move to reflect the breadth of their audiences and the diversity of their perspectives, so must museum documentation change to reflect concerns other than the traditionally art historical and museological.

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Section: Content [Image] Based Retrievalmentioning

confidence: 99%

Social Classification and Folksonomy in Art Museums: Early Data From the STEVE.MUSEUM Tagger Prototype

Trant¹

2006

Advances in Classification Research Online

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“…The work in [34] illustrates the use of computer vision techniques for the development of content-based image retrieval system for historic glass and an automatic system for mediaeval coin classification. In another work by [36] a prototype is presented which allows the end-user to search for similar digital library objects based on the image content. An integrated content and metadata-based retrieval system for art images in the domain of museum and gallery image collections is presented in [22].…”

Section: Related Workmentioning

confidence: 99%

Image-based search and retrieval for biface artefacts using features capturing archaeologically significant characteristics

Eramian

Walia

Power

et al. 2016

Machine Vision and Applications

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Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge. Often, the metadata associated with images is sparse or is inconsistent. This makes keyword-based exploratory search difficult, leaving researchers to rely on serendipity and slowing down the research process. We present an image-based retrieval system that addresses this problem for biface artefacts. In order to identify artefact characteristics that need to be captured by image features, we conducted a contextual inquiry study with experts in bifaces. We then devised several descriptors for matching images of bifaces with similar artefacts. We evaluated the performance of these descriptors using measures that specifically look at the differences between the sets of images returned by the search system using different descriptors.

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“…Regardless of the past rates of interest and adoption of CBIR systems, researchers have been hard at work developing systems that could have real‐world applications for these academic users. Several recent studies discuss CBIR systems that were developed with archaeological materials (Kadobayashi & Tanaka, ; Vemuri et al., ; Wang, Ge, Snow, Mitra, & Giles, ) and art historical applications in mind (Barni, Pelagotti, & Piva, ; Li, ; Li & Wang, ; Li, Yao, Hendriks, & Wang, ; Stanchev, Green, & Dimitrov, ; Yao, Li, & Wang, ). These researchers see CBIR technologies as providing unique avenues for the study of cultural materials.…”

Section: Literature Reviewmentioning

confidence: 99%

Content‐based image retrieval methods and professional image users

Beaudoin

2015

Asso for Info Science & Tech

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This article reports the findings of a qualitative research study that examined professional image users' knowledge of, and interest in using, content-based image retrieval (CBIR) systems in an attempt to clarify when and where CBIR methods might be applied. The research sought to determine the differences in the perceived usefulness of CBIR technologies among image user groups from several domains and explicate the reasons given regarding the utility of CBIR systems for their professional tasks. Twenty participants (archaeologists, architects, art historians, and artists), individuals who rely on images of cultural materials in the performance of their work, took part in the study. The findings of the study reveal that interest in CBIR methods varied among the different professional user communities. Individuals who showed an interest in these systems were primarily those concerned with the formal characteristics (i.e., color, shape, composition, and texture) of the images being sought. In contrast, those participants who expressed a strong interest in images of known items, images illustrating themes, and/or items from specific locations believe concept-based searches to be the most direct route. These image users did not see a practical application for CBIR systems in their current work routines.

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A Content-Based Image Retrieval Service for Archaeology Collections

Cited by 4 publications

References 3 publications

Social Classification and Folksonomy in Art Museums: Early Data From the STEVE.MUSEUM Tagger Prototype

Social Classification and Folksonomy in Art Museums: Early Data From the STEVE.MUSEUM Tagger Prototype

Image-based search and retrieval for biface artefacts using features capturing archaeologically significant characteristics

Content‐based image retrieval methods and professional image users

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