Majority Voting Re-ranking Algorithm for Content Based-Image Retrieval

Mosbah, Mawloud; Boucheham, Bachir

doi:10.1007/978-3-319-24129-6_11

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…In order to combine evidence gathered from the Top-N content-based most similar geotagged tweets to a non-geotagged tweet t ng , we adopt a weighted majority voting algorithm [35,36,37,38,39,40] as follows. Each element of the Top-N tweets is represented as a tuple (t i , l i , u i ), where l i is the location associated with the geotagged tweet t i posted by the user u i .…”

Section: Weighted Majority Votingmentioning

confidence: 99%

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On fine-grained geolocalisation of tweets and real-time traffic incident detection

Paule

Sun

Moshfeghi

2019

Information Processing & Management

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Recently, geolocalisation of tweets has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geotagged tweets available to enable such tasks remains insufficient. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data and has consequences for a wide range of applications. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets, where each vote is weighted based on evidence gathered from the ranking. Using geotagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) significantly outperforms state-ofthe-art baselines in terms of accuracy and error distance, in both cities, with the cost of decreased coverage. Finally, we investigated the applicability of our method in a real-time scenario by means of a traffic incident detection task. Our analysis shows that our fine-grained geolocalisation method can overcome the limitations of geotagged tweets and precisely map incident-related tweets at the real location of the incident.

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Section: Weighted Majority Votingmentioning

confidence: 99%

“…The majority voting algorithm is a well known, fast and effective strategy widely adopted for prediction and re-ranking tasks [35,36,37]. However, to the best of our knowledge, this is the first time the majority voting is considered to tackle the geolocation of tweets.…”

Section: Introductionmentioning

confidence: 99%

On fine-grained geolocalisation of tweets and real-time traffic incident detection

Paule

Sun

Moshfeghi

2019

Information Processing & Management

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“…In this subsection, we address (information need, query) problem as a preprocessing operation in order to well deal with the (query, index) problem. In other words, we are interested here in query expansion and reformulation [19], [20].…”

Section: Query Expansion and Query Reformulationmentioning

confidence: 99%

Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

Mosbah

2018

JIOS

Self Cite

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In this paper, we address the enhancing of Google Scholar engine, in the context of text retrieval, through two mechanisms related to the interrogation protocol of that query expansion and reformulation. The both schemes are applied with re-ranking results using a pseudo relevance feedback algorithm that we have proposed previously in the context of Content based Image Retrieval (CBIR) namely Majority Voting Re-ranking Algorithm (MVRA). The experiments conducted using ten queries reveal very promising results in terms of effectiveness.

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“…In order to combine evidences gathered from the Top-N contentbased similar geo-tagged tweets to a non-geo-tagged tweet t n , we adopted a weighted majority voting algorithm [4,12,16] as follows.…”

Section: Combining Evidence Using Weighted Majority Votingmentioning

confidence: 99%

“…work, we incorporate this score as a weight of each vote in our adopted majority voting approach. e majority voting algorithm is a well known, fast and e ective strategy widely adopted for prediction and re-ranking tasks [4,12,16]. However, to the best of our knowledge, this is the rst time the majority voting is considered to tackle the geo-location of tweets.…”

Section: Introductionmentioning

confidence: 99%

On Fine-Grained Geolocalisation of Tweets

Paule

Moshfeghi

Jose

et al. 2017

Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval

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Recently, the geolocalisation of tweets has become an important feature for a wide range of tasks in Information Retrieval and other domains, such as real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geo-tagged tweets available remains insu cient to reliably perform such tasks. us, predicting the location of non-geotagged tweets is an important yet challenging task, which can increase the sample of geo-tagged data and help to a wide range of tasks. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets weighted based on the credibility of its source (Twi er user). Using geo-tagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) signi cantly outperforms our baselines in terms of accuracy, and error distance, in both cities, with the cost of decrease in recall.

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Majority Voting Re-ranking Algorithm for Content Based-Image Retrieval

Cited by 4 publications

References 29 publications

On fine-grained geolocalisation of tweets and real-time traffic incident detection

On fine-grained geolocalisation of tweets and real-time traffic incident detection

Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

On Fine-Grained Geolocalisation of Tweets

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