An adaptive teleportation random walk model for learning social tag relevance

Zhu, Xiaofei; Nejdl, Wolfgang; Géorgescu, Mihai

doi:10.1145/2600428.2609556

Cited by 35 publications

(21 citation statements)

References 27 publications

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“…Specifically, they adopted the Rankboost [101] to learn an optimal combination of these multi-modality correlations, and generated a ranking function for tag recommendation. Recently, Zhu et al [102] proposed a method of tag recommendation based on the neighbor voting graph of tags. They casted the social tag relevance learning problem as an adaptive teleportation random walk process on the voting graph.…”

Section: Topics and Tags Recommendationmentioning

confidence: 99%

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Bouadjenek

Hacid

Bouzeghoub

2016

Information Systems

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a b s t r a c tThere is currently a number of research work performed in the area of bridging the gap between Information Retrieval (IR) and Online Social Networks (OSN). This is mainly done by enhancing the IR process with information coming from social networks, a process called Social Information Retrieval (SIR). The main question one might ask is What would be the benefits of using social information (no matter whether it is content or structure) into the information retrieval process and how is this currently done?With the growing number of efforts towards the combination of IR and social networks, it is necessary to build a clearer picture of the domain and synthesize the efforts in a structured and meaningful way. This paper reviews different efforts in this domain. It intends to provide a clear understanding of the issues as well as a clear structure of the contributions. More precisely, we propose (i) to review some of the most important contributions in this domain to understand the principles of SIR, (ii) a taxonomy to categorize these contributions, and finally, (iii) an analysis of some of these contributions and tools with respect to several criteria, which we believe are crucial to design an effective SIR approach. This paper is expected to serve researchers and practitioners as a reference to help them structuring the domain, position themselves and, ultimately, help them to propose new contributions or improve existing ones.

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Section: Topics and Tags Recommendationmentioning

confidence: 99%

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Bouadjenek

Hacid

Bouzeghoub

2016

Information Systems

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“…These methods are nonparametric, and the complexity of the learned hypotheses grows as the amount of training data increases. The neighbor voting algorithm [Li et al 2009b] and its variants [Kennedy et al 2009;Truong et al 2012;Lee et al 2013;Zhu et al 2014] estimate the relevance of a tag t with respect to an image x by counting the occurrence of t in annotations of the visual neighbors of x. The visual neighborhood is created using features obtained from early fusion of global features [Li et al 2009b], distance metric learning to combine local and global features [Verbeek et al 2010;Wu et al 2011], cross-modal learning of tags and image features [Qi et al 2012;Ballan et al 2014;Pereira et al 2014], and fusion of multiple single-feature learners Li 2016].…”

Section: Learning For Tag Relevancementioning

confidence: 99%

“…For instance, in Truong et al [2012] and Lee et al [2013], the visual similarity is used as the weights. As an alternative to such a heuristic strategy, Zhu et al [2014] model the relationships among the neighbors by constructing a directed voting graph, wherein there is a directed edge from image x i to image x j if x i is in the k nearest neighbors of x j . Subsequently, an adaptive random walk is conducted over the voting graph to estimate the tag relevance.…”

Section: Learning For Tag Relevancementioning

confidence: 99%

“…Subsequently, an adaptive random walk is conducted over the voting graph to estimate the tag relevance. However, the performance gain obtained by these weighting strategies appears to be limited [Zhu et al 2014]. The kernel density estimation technique used in Liu et al [2009] can be viewed as another form of weighted voting, but the votes come from images labeled with t instead of the visual neighbors.…”

Section: Learning For Tag Relevancementioning

confidence: 99%

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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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“…Tag  Zhu et al [13], proposed graph voting. Graph voting is an oriented graph in which the nodes are annotated images by t tag in X. there…”

Section: B Tag Retrievingmentioning

confidence: 99%

A Review of Methods of Instance-based Automatic Image Annotation

Derakhshan¹,

Maihami²

2016

IJISA

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Abstract-Today, to use automatic image annotation in order to fill the semantic gap between low level features of images and understanding their information in retriev ing process has become popular. Since automatic image annotation is crucial in understanding digital images several methods have been proposed to automatically annotate an image. One of the most important of these methods is instance-based image annotation. As these methods are vastly used in this paper, the most impo rtant instance-based image annotation methods are analy zed. First of all the main parts of instance-based automatic image annotation are analyzed. Afterwards, the main methods of instance-based automatic image annotation are reviewed and co mpared based on various features. In the end the most important challenges and open-ended fields in instance-based image annotation are analyzed.

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An adaptive teleportation random walk model for learning social tag relevance

Cited by 35 publications

References 27 publications

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Socializing the Semantic Gap

A Review of Methods of Instance-based Automatic Image Annotation

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