Tag Suggestr: Automatic Photo Tag Expansion Using Visual Information for Photo Sharing Websites

Küçüktunç, Onur; Sevil, S.; Tosun, Akif Burak; Zitouni, Hilal; Duygulu, Pınar; Can, Fazlı

doi:10.1007/978-3-540-92235-3_7

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…The co-occurrence is computed over large user collections and may lose relevance in the local context of users. The Tag Suggestr [11] system based on a combination of visual and text features, uses the initial set of tags to retrieve additional tags from related photos that have some of the initial tags in their tag lists. The candidate tags are weighted using visual similarity between their original images and the useruploaded photo.…”

Section: Related Workmentioning

confidence: 99%

Quest for relevant tags using local interaction networks and visual content

Sawant

Datta

et al. 2010

Proceedings of the International Conference on Multimedia Information Retrieval

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Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Naïve Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.

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Section: Related Workmentioning

confidence: 99%

Quest for relevant tags using local interaction networks and visual content

Sawant

Datta

et al. 2010

Proceedings of the International Conference on Multimedia Information Retrieval

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“…The underlying labels were utilized to recover extra labels from related photographs that had a portion of the underlying labels in their label records. String et al [19] proposed a system that finds area agent labels from travelogues and afterward select pertinent and delegate photographs to picture these labels…”

Section: Related Workmentioning

confidence: 99%

RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues

Maheswari¹,

Sathiaseelan²

2018

IJET

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The travelogues contain prosperous geo-referenced information such as tours, weather, and expenses etc. Reading travelogues and finding useful trip information is a tedious task for many people. So as to correlate the georeferenced text meaningfully the semantic ranking is taken in account on the user contributing travelogues. In this way there is a requirement for creating programmed travelogue mining methods to pass on valuable data in a travelogue to its peruses in the more successful way. Visualizing a geo-referenced data with location tags and descriptions makes it helpful for readers to understand the fundamental substance of the travelogue. For user expedient, this paper proposes a Travelogue RankTagViz approach that semantically ranks and visualizes the tags of user travelogues. The RankTagViz contains two phases. In the first phase, the user travelogues are ranked based on semantic. In the next phase, the travelogues are visualized based on the tag and images. During semantic ranking, a semantic dimension reduction method is proposed to pre-process and mine useful information from the travelogues. After that a semantic rank mechanism is proposed to rank travelogues based on tags and POI (Point Of Interest). For tag visualization, location based tags and images are extracted from the travelogues and a novel UI is intended to give a superior client encounter, by arranging both the literary and visual data produced by committed voyagers in an alluring way. Test comes about on an arrangement of gathered travelogues show the proposed techniques' capacity to rank and imagine travelogues.

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“…Through the combination of textual information with vision based methods efficient systems for tag recommendation and propagation can be developed. Kucuktunc et al [10] incorporate visual and textual cues of semantically relevant photos to recommend tags and to improve the quality of the suggested tags. First, the system requires the user to add a few initial tags for each uploaded photo.…”

Section: Related Workmentioning

confidence: 99%

Object-based tag propagation for semi-automatic annotation of images

Ivanov

Vajda

Goldmann

et al. 2010

Proceedings of the International Conference on Multimedia Information Retrieval

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Over the last few years, social network systems have greatly increased users' involvement in online content creation and annotation. Since such systems usually need to deal with a large amount of multimedia data, it becomes desirable to realize an interactive service that minimizes tedious and timeconsuming manual annotation. In this paper, we propose an interactive online platform that is capable of performing semi-automatic image annotation and tag recommendation for an extensive online database. First, when the user marks a specific object in an image, the system performs an object duplicate detection and returns the search results with images containing similar objects. Then, the annotation of the object can be performed in two ways: (1) In the tag recommendation process, the system recommends tags associated with the object in images of the search results, among which, the user can accept some tags for the object in the given image. (2) In the tag propagation process, when the user enters his/her tag for the object, it is propagated to images in the search results. Different techniques to speed-up the process of indexing and retrieval are presented in this paper and their effectiveness demonstrated through a set of experiments considering various classes of objects.

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Tag Suggestr: Automatic Photo Tag Expansion Using Visual Information for Photo Sharing Websites

Cited by 16 publications

References 10 publications

Quest for relevant tags using local interaction networks and visual content

Quest for relevant tags using local interaction networks and visual content

RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues

Object-based tag propagation for semi-automatic annotation of images

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