I know what you did last summer: object-level auto-annotation of holiday snaps

Gammeter, Stephan; Bossard, Lukas; Quack, Till; Gool, Luc Van

doi:10.1109/iccv.2009.5459180

Cited by 80 publications

(116 citation statements)

References 23 publications

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“…Finally, Wikipedia 8 articles are attached to the images and the validity of these associations is checked. Gammeter et al [9] extends this idea towards object-based auto-annotation of holiday photos in a large database that includes landmark buildings, statues, scenes, pieces of art, with help of external resources such as Wikipedia. In both [20] and [9], GPS coordinates are used to pre-cluster objects which may not be always available.…”

Section: Combined Analysis Of Geographical Context and Visual Contentmentioning

confidence: 99%

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Geotag propagation in social networks based on user trust model

Ivanov

Vajda

Lee

et al. 2010

Multimed Tools Appl

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In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks.

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Section: Combined Analysis Of Geographical Context and Visual Contentmentioning

confidence: 99%

“…Massa and Avesani [17] compared and evaluated user trust models on their webbased application presented online at Epinions. 9 They considered two kinds of user trust models, global and local trust. The global trust model assigns a trust value to each user, independently of who is evaluating the other users.…”

Section: User Trust Modelingmentioning

confidence: 99%

Geotag propagation in social networks based on user trust model

Ivanov

Vajda

Lee

et al. 2010

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Finally, Wikipedia 8 articles are attached to the images, and the validity of these associations is checked. Gammeter et al [6] extend this idea toward objectbased auto-annotation of holiday photos in a large database that includes landmark buildings, statues, scenes, and pieces of art, with the help of external resources such as Wikipedia. In both [33] and [6], GPS coordinates are used to pre-cluster objects which may not be always available.…”

Section: Geotagging In Social Network and Sharing Websitesmentioning

confidence: 99%

“…The authors perform clustering on GPS coordinates and visual texture features from the image pool and extract landmark names as the most frequent tags associated with the particular visual cluster. Additionally, they extract landmark names from the travel guide articles, such as Wikitravel, 6 and visually cluster photos gathered by querying Google Images. 7 However, the test set they use is quite limited -728 images in total for a 124-category problem or less than six test images per landmark.…”

Section: Geotagging In Social Network and Sharing Websitesmentioning

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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“…They handle tags by a modified TF-IDF ranking and link their results to Wikipedia 2 . Gammeter et al [4] overlay a geospatial grid over earth and match pairwise retrieved photos of each tile using visual features. Then cluster photos into groups of images depicting the same scene.…”

Section: A Using Both Visual Descriptions and Textual Metadatamentioning

confidence: 99%

Placing User-Generated Photo Metadata on a Map

Spyrou

Mylonas

2011

2011 Sixth International Workshop on Semantic Media Adaptation and Personalization

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Abstract-In this paper we analyze large user photo collections from Flickr in order to select the most appropriate tags to describe a geographical area. We cluster photos based on their latitude and longitude and divide large areas into smaller clusters, which we will refer to as "geo-clusters". Geo-clusters have a fixed size and are able to overlap. They do not cover the entire area of interest, omitting parts where no single photo has been geo-tagged at. Within each geo-cluster we analyze all collected textual metadata i.e. the user selected tags of the photos it contains. We are then able to rank them and select the most appropriate that are able to describe landmarks and other places of interest that are contained within. Finally we place these tags on a map to help users to intuitevely understand places of interest/visual content at a glance.

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I know what you did last summer: object-level auto-annotation of holiday snaps

Cited by 80 publications

References 23 publications

Geotag propagation in social networks based on user trust model

Geotag propagation in social networks based on user trust model

Geotag Propagation with User Trust Modeling

Placing User-Generated Photo Metadata on a Map

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