Robust Duplicate Detection of 2D and 3D Objects

Vajda, Péter; Ivanov, Ivan; Goldmann, Lutz; Lee, Jong-Seok; Ebrahimi, Touradj

doi:10.4018/jmdem.2010070102

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…Considering only object duplicate detection [24] in the tag propagation scenario without trust modeling, we compute the recognition rate for all landmarks in the dataset as shown in Fig. 5.…”

Section: A Results Of the Tag Propagation Scenariomentioning

confidence: 99%

“…Second, for tag propagation scenario using object duplicate detection (ODD) method [24], and are calculated as:…”

Section: Discussionmentioning

confidence: 99%

“…It contains three functional modules, each of which has a specific task: object duplicate detection, tag propagation, and user trust modeling. As the focus of this paper is on trust modeling, the object duplicate detection [24] and tag propagation [4] modules are only summarized briefly below. The user trust modeling module is powered by alternating the five trust models described in Section III, and then the performance of the trust module and the whole system is measured, and results are reported and analyzed in Section VI.…”

Section: An Automated Landmark Taggingmentioning

confidence: 99%

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Comparative Study of Trust Modeling for Automatic Landmark Tagging

Ivanov

Vajda

Korshunov

et al. 2013

IEEE Trans.Inform.Forensic Secur.

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Abstract-Many images uploaded to social networks are related to travel, since people consider traveling to be an important event in their life. However, a significant amount of travel images on the Internet lack proper geographical annotations or tags. In many cases, the images are tagged manually. One way to make this timeconsuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In this paper, we present a system for automatic geotag propagation in images based on the similarity between image content (famous landmarks) and its context (associated geotags). In such a scenario, however, an incorrect or a spam tag can damage the integrity and reliability of the automated propagation system. Therefore, for reliable geotags propagation, we suggest adopting a user trust model based on social feedback from the users of the photo-sharing system. We compare this socially-driven approach with other user trust models via experiments and subjective testing on an image database of various famous landmarks. Results demonstrate that relying on user feedback is more efficient, since the number of propagated tags more than doubles without loss of accuracy compared to using other models or propagating without trust modeling.

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Section: A Results Of the Tag Propagation Scenariomentioning

confidence: 99%

“…Second, for tag propagation scenario using object duplicate detection (ODD) method [24], and are calculated as:…”

Section: Discussionmentioning

confidence: 99%

Section: An Automated Landmark Taggingmentioning

confidence: 99%

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Comparative Study of Trust Modeling for Automatic Landmark Tagging

Ivanov

Vajda

Korshunov

et al. 2013

IEEE Trans.Inform.Forensic Secur.

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show abstract

“…It contains three functional modules, each of which has a specific task: object duplicate detection, tag propagation, and user trust modeling. As the focus of this chapter is on trust modeling, the object duplicate detection [38] and tag propagation [13] modules are only summarized briefly below.…”

Section: An Automated Geotag Propagation Systemmentioning

confidence: 99%

Geotag Propagation with User Trust Modeling

Ivanov

Vajda

Lee

et al. 2012

Computer Communications and Networks

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The amount of information that people share on social networks is constantly increasing. People also comment, annotate, and tag their own content (videos, photos, notes, etc.), as well as the content of others. In many cases, the content is tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In such a scenario, however, a wrong or a spam tag can damage the integrity and reliability of the automated propagation system. Users may make mistakes in tagging, or irrelevant tags and content may be added maliciously for advertisement or self-promotion. Therefore, a certain mechanism insuring the trustworthiness of users or published content is needed. In this chapter, we discuss several image retrieval methods based on tags, various approaches to trust modeling and spam protection in social networks, and trust modeling in geotagging systems. We then consider a specific example of automated geotag propagation system that adopts a user trust model. The tag propagation in images relies on the similarity between image content (famous landmarks) and its context (associated geotags). For each tagged image, similar untagged images are found by the robust graph-based object duplicate detection, and the known tags are propagated accordingly. The user trust value is estimated based on a social feedback from the users of the photo-sharing system, and only tags from trusted users are propagated. This approach demonstrates that a practical tagging system significantly benefits from the intelligent combination of efficient propagation algorithm and a user-centered trust model.

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Robust Duplicate Detection of 2D and 3D Objects

Cited by 2 publications

References 25 publications

Comparative Study of Trust Modeling for Automatic Landmark Tagging

Comparative Study of Trust Modeling for Automatic Landmark Tagging

Geotag Propagation with User Trust Modeling

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