Enhancing Visual Resources for Searching and Retrieval--Is Content-based Image Retrieval a Solution?

Graham, Margaret E.

doi:10.1093/llc/19.3.321

Cited by 5 publications

(5 citation statements)

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“…In their study, comments about the visually similar, but topically dissonant, images retrieved from the CBIR system were received from the artist and architect participants and they were noted as being positive in nature (Matkovic et al., ) . The positive user evaluations of the CBIR system, even with imperfect retrieval results, mirrors what was found by Graham ().…”

Section: Literature Reviewsupporting

confidence: 82%

“…Examination of user interest in CBIR systems is acknowledged as being under‐researched (Datta et al., ; Enser, ; Graham, ; Kherfi et al., ). This study found that individuals who showed interest in these systems are primarily those concerned with the formal characteristics (i.e., color, shape, composition, and texture) of what was being sought.…”

Section: Discussionmentioning

confidence: 99%

“…Sifting through pages and pages of retrieved images to find a simple general view of a structure or object was found to be highly frustrating. Graham () suggests that CBIR methods might help in this area. She reports the findings of a study conducted of an online image collection with a CBIR‐enabled search that surveyed individuals about the usefulness of the system and their satisfaction with the retrieval results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is clear that additional usability research into image systems using content‐ and concept‐based retrieval methods remains to be done. As Graham () states, “[n]ot much research has been done involving subjective measures such as user satisfaction and user acceptability” (p. 329). Ménard and Khashman () suggest that CBIR systems could prove useful to image seekers, especially in multilingual contexts when language fails to retrieve desired content.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Of particular interest to this study are image users' thoughts and beliefs on the utility of these systems to their work. Because user studies in the area of CBIR technologies have been limited (Datta, Joshi, Li, & Wang, ; Enser, ; Graham, ; Kherfi, Ziou, & Bernardi, ), this study's primary contribution is its first‐hand accounts of potential users' ideas concerning these systems.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Content‐based image retrieval methods and professional image users

Beaudoin

2015

Asso for Info Science & Tech

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Object-Based Image Retrieval System Using Rough Set Approach

Ghali

Abd-Elmonim

Hassanien

2012

Advances in Reasoning-Based Image Processing Intelligent Systems

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Classification and Retrieval of Images from Databases Using Rough Set Theory

Hassanien

Ali

2009

Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications

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This chapter presents an efficient algorithm to classify and retrieve images from large databases in the context of rough set theory. Color and texture are two well-known low-level perceptible features to describe an image contents used in this chapter. The features are extracted, normalized, and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. We test three different popular distance measures in this work and find that quadratic distance measures provide the most accurate and perceptually relevant retrievals. The retrieval performance is measured using recall-precision measure, as is standard in all retrieval systems.

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Enhancing Visual Resources for Searching and Retrieval--Is Content-based Image Retrieval a Solution?

Cited by 5 publications

References 0 publications

Content‐based image retrieval methods and professional image users

Content‐based image retrieval methods and professional image users

Object-Based Image Retrieval System Using Rough Set Approach

Classification and Retrieval of Images from Databases Using Rough Set Theory

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