On contextual photo tag recommendation

McParlane, Philip J.; Moshfeghi, Yashar; Jose, Joemon M.

doi:10.1145/2484028.2484160

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…time and location) has been exploited for tag recommendation and visualisation purposes: Zhang et al [23] cluster tags based on geolocation and temporal trends allowing for the construction of tag cluster visualisations. McParlane et al [2] exploit the daily, monthly and yearly trends of tags for tag recommendation purposes.…”

Section: Image Context and Contentsmentioning

confidence: 99%

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

McParlane

Moshfeghi

Jose

2014

Proceedings of International Conference on Multimedia Retrieval

Self Cite

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Predicting popular content is a challenging problem for social media websites in order to encourage user interactions and activity. Existing works in this area, including the recommendation approach used by Flickr (called "interestingness 1 "), consider only click through data, tags, comments and explicit user feedback in this computation. On image sharing websites, however, many images are annotated with no tags and initially, an image has no interaction data. In this case, these existing approaches fail due to lack of evidence. In this paper, we therefore focus on image popularity prediction in a cold start scenario (i.e. where there exist no, or limited, textual/interaction data), by considering an image's context, visual appearance and user context. Specifically, we predict the number of comments and views an image has based on a number of new features for this propose. Experimenting on the MIR-Flickr 1M collection, we are able to overcome the problems associated with popularity prediction in a cold start, achieving accuracy of up to 76%.

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Section: Image Context and Contentsmentioning

confidence: 99%

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

McParlane

Moshfeghi

Jose

2014

Proceedings of International Conference on Multimedia Retrieval

Self Cite

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“…Individual user tagging history has been studied before with application to Factorization Machines in [47]. Alternatively, [48] used the tagging history data corresponding to the image location and time. Further, the role of context has been explored in [49] and [50], these works are mainly focused on single tag prediction [49] and are trained on smaller datasets with a small set of lab curated labels [50].…”

Section: Related Workmentioning

confidence: 99%

Adversarial Learning for Personalized Tag Recommendation

Quintanilla

Rawat

Sakryukin

et al. 2021

IEEE Trans. Multimedia

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We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the existing work focuses on single-label image classification. However, there are usually multiple tags associated with an image. The existing works on multi-label classification are mainly based on lab curated labels. Humans assign tags to their images differently, which is mainly based on their interests and personal tagging behavior. In this paper, we address the problem of personalized tag recommendation and propose an end-to-end deep network which can be trained on large-scale datasets. The user-preference is learned within the network in an unsupervised way where the network performs joint optimization for user-preference and visual encoding. A joint training of user-preference and visual encoding allows the network to efficiently integrate the visual preference with tagging behavior for a better user recommendation. In addition, we propose the use of adversarial learning, which enforces the network to predict tags resembling usergenerated tags. We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE. The proposed method achieves significantly better performance on both the datasets when compared to the baselines and other state-of-the-art methods. The code is publicly available at https://github.com/vyzuer/ALTReco.

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“…In McParlane et al [2013b], time-constrained tag co-occurrence statistics are considered to refine the output of visual classifiers for tag assignment. In their follow-up work [McParlane et al 2013a], location-constrained tag co-occurrence computed from images Johnson et al [2015], social network metadata such as image groups membership or contacts of users is employed to resolve ambiguity in visual appearance. Comparing the three groups, tag + image appears to be the mainstream, as evidenced by the imbalanced distribution in Table I.…”

Section: Media For Tag Relevancementioning

confidence: 99%

Socializing the Semantic Gap

et al. 2016

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Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.

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On contextual photo tag recommendation

Cited by 10 publications

References 9 publications

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

Adversarial Learning for Personalized Tag Recommendation

Socializing the Semantic Gap

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