Location-Aware Tag Recommendations for Flickr (original) (raw)

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PERSONALIZED GEO-TAG RECOMMENDATION FOR COMMUNITY CONTRIBUTED IMAGES

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Automatic Photo tagging based on Geo-Tags in Social Sites

Photo tagging is becoming more and more consequential now-a-days to organize and search astronomically immense number of photos on convivial websites. To engender high quality convivial tags and automatic tag recommendation is the main research topic. In this paper main focus is on the personalized and geo-categorical tag recommendation. Consider users and geo locations have different preferred tags assigned to a photo, an incipient subspace learning method is proposed to individually discover both the predilections. The goal is learn coalesced space which is shared by visual domain and textual domain to make visual features and textual features commensurable. Visual feature is considered to be lower caliber representation on semantics than textual feature. Supplement ally intermediate space is introduced for the visual space and expecting it to have consistent local structure with text space. Cumulated space is mapped from the textual space and the intermediate space respectively. When an untagged photo with its geo-location is given predicated on the most proximate neighboring search utilizer preferred and geo-location-concrete tags are found in the corresponding cumulated space. Then cumulate these obtained tags and the visual appearance of the photo to find semantically and visually homogeneous photos, among which the most frequently used tags are suggested to the utilizer and utilizer is sanctioned to cull predicated on his predilection. Conclusively, the tags cognate to the keywords, geo-location and utilizer profile information are recommended to the utilizer automatically.

A Survey of Geo-tagged Multimedia Content Analysis within Flickr

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Our survey paper attempts to investigate how recent and undoubted emerge in enriched, geo-tagged social networks' multimedia content sharing works to the benefit of their users and whether it could be handled in a formal way, in order to capture the meaningful semantics rising from this newly introduced user experience. It further specializes its focus by providing an overview of current state-of-the-art techniques with respect to geo-tagged content access, processing and manipulation within the popular Flickr social network. In this manner it explores the role of information retrieval, integration and extraction from the technical point of view, coupled together with human social network activities, like, for instance, localization and recommendations based on pre-processed collaborative geo-tagged photos, resulting into more efficient, optimized search results.

Large Scale Tag Recommendation Using Different Image Representations

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Nowadays, geographical coordinates (geo-tags), social annotations (tags), and low-level features are available in large image datasets. In our paper, we exploit these three kinds of image descriptions to suggest possible annotations for new images uploaded to a social tagging system. In order to compare the benefits each of these description types brings to a tag recommender system on its own, we investigate them independently of each other. First, the existing data collection is clustered separately for the geographical coordinates, tags, and low-level features. Additionally, random clustering is performed in order to provide a baseline for experimental results. Once a new image has been uploaded to the system, it is assigned to one of the clusters using either its geographical or low-level representation. Finally, the most representative tags for the resulting cluster are suggested to the user for annotation of the new image. Large-scale experiments performed for more than 400,000 images compare the different image representation techniques in terms of precision and recall in tag recommendation.

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We analyse and combine a number of worldwide crowd-sourced geotagged databases with the goal to locate, describe and rate potential tourism targets in any area in the world. In particular, we address the problem of finding representative names and top POIs for popular areas, with the main focus on sightseeing. The results are demonstrated on the sightsmap.com site presenting a zoomable and pannable tourism popularity heat map along with popularity-sorted POI markers for concrete objects.

Personalized, interactive tag recommendation for flickr

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We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust.

Personalized, interactive tag recommendation for flickr. In RecSys

2008

We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust. Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a use...

Geotagging Flickr Photos And Videos Using Language Models

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This paper presents an experimental framework for the Placing tasks, both estimation and verification at MediaEval Benchmarking 2016. The proposed framework provides results for four runs first, using metadata (such as user tags and title of images and videos), second, using visual features extracted from the images (such as tamura), third, by using the textual and visual features together and fourth, using metadata as in the first run but with the training data augmented with external sources. Our work mainly focusses on textual features where we develop a language-based model using bag-of-tags with neighbour based smoothing. The effectiveness of the framework is evaluated through experiments in the placing task.

Automated Extraction and Geographical Structuring of Flickr Tags

The volume and potential value of user generated content (UGC) is ever growing. One such source is geotagged images on Flickr. Typically, images on Flickr are tagged with location and attribute information variously describing location, events or objects in the image. Though inconsistent and 'noisy', the terms can reflect concepts at a range of geographic scales. From a spatial data integration perspective, the information relating to 'place' is of primary interest and the challenge is in selecting the most appropriate tag(s) that best describe the geography of the image. This paper presents a methodology for searching among the 'tag noise' in order to identify the most appropriate tags across a range of scales, by varying the size of the sampling area within which Flickr imagery falls. This is applied in the context of urban environments. Empirical analysis was then used to assess the correctness of the chosen tags (whether the tag correctly described the ge...

A WORLDWIDE TOURISM RECOMMENDATION SYSTEM BASED ON GEOTAGGED WEB PHOTOS

This work aims to build a system to suggest tourist destinations based on visual matching and minimal user input. A user can provide either a photo of the desired scenary or a keyword describing the place of interest, and the system will look into its database for places that share the visual characteristics. To that end, we first cluster a large-scale geotagged web photo collection into groups by location and then find the representative images for each group. Tourist destination recommendations are produced by comparing the query against the representative tags or representative images under the premise of "if you like that place, you may also like these places".