Event Detection Refinement Using External Tags for Flickr Collections

Selvam, Sheba; Ramasamy, Balakrishnan; Balasundaram, S. R.

doi:10.1007/978-81-322-1665-0_35

Cited by 5 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [19], an event is defined as a 3-tuple <E, R, t>, where E is a set of entities, R E x E is a set of dynamic relationships, t is a continuous time window. Other definitions have provided categories for events [21]: local and global events, home and away-from-home events, routine and non-routine events, etc. Global events build collective experiences that permit the sharing of personal experiences as part of a more social phenomenon called collective events [11].…”

Section: A Event Definitionmentioning

confidence: 99%

“…Recently, numerous studies have investigated the problem of event detection from images. They can be classified in two groups: event clustering approaches [5][6][7][8][9][10][11] and event hybrid approaches [12,14,17,21,27,28]. Extracting events from multimedia in terms of photographs or images is much more difficult when compared to text for essentially two reasons: i) Event detection from images requires aggregation of heterogeneous metadata [29]; ii) Linking multimedia data to event model aspects is far more challenging then textual data [30].…”

Section: B Event Detection From Multimediamentioning

confidence: 99%

“…• Lack of event linking: Existing approaches do not consider relationships between events neither the multi-* property of an event (e.g., multi-day events, multi-site events, multi-person events, etc.). • Restriction of the image extraction to the user's personal profile: Most of the existing approaches [5,11,21,32] limit the personal space to the user's profile.…”

Section: Event Detection In Online Social Networkmentioning

confidence: 99%

See 2 more Smart Citations

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

Raad

Chbeir

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

View full text Add to dashboard Cite

Online social networking has become the predominant activity in the digital world thanks to multimedia data (mainly photos) sharing (e.g., photos now represent 93% of the top posts on Facebook). Discovering events where users are involved using their own posts and those shared by their friends would be of great importance. In this paper, we address this issue by providing an original approach able to detect, enrich and also link user's events using photos shared within his online social networks. Using metadata, our approach provides a multidimensional gathering of similar photos using their temporal, geographical, and social facets. To validate our approach, we implemented a prototype called Foto2Events and conducted a set of experiments on real and generated data. Results show that our approach works well for various metadata distributions.

show abstract

Section: A Event Definitionmentioning

confidence: 99%

Section: B Event Detection From Multimediamentioning

confidence: 99%

Section: Event Detection In Online Social Networkmentioning

confidence: 99%

See 1 more Smart Citation

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

Raad

Chbeir

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

View full text Add to dashboard Cite

show abstract

“…In this section, we review event definitions, types, and features before detailing event detection works in different areas (e.g., social event detection, medical event detection, sensor event detection). Since, in most cases, events are detected based on incoming raw data, without having any prior knowledge on the occurring events, main approaches [8,28,31,32,37] use unsupervised clustering techniques. Since there are no commonly adopted criteria, we propose the following set of criteria to compare the referenced works:…”

Section: Related Workmentioning

confidence: 99%

“…To achieve this, many works [8,9,32,36,37] have evolved around the organization of shared data on platforms and applications using clustering techniques. Some social-based studies [8,32,35,37] are based on metadata (e.g., Facebook organizes multimedia content based on publishing timestamps, Iphotos combines photo creation timestamps and locations to organize a user's photo library). Others [26,28,31] use the visual attributes of shared objects (e.g., textures, colors) coupled with metadata in order to detect several events.…”

Section: Introductionmentioning

confidence: 99%

eVM: An Event Virtual Machine Framework

Mansour

Chbeir

Arnould

2018

Transactions on Large-Scale Data- And Knowledge-Centered Systems XXXIX

View full text Add to dashboard Cite

Information and communication technology (ICT) is impacting our daily lives more than ever before. Many existing applications guide users in their daily activities (e.g., navigation through traffic, health monitoring, managing home comfort, socializing with others). Although these applications are different in terms of purpose and application domain, they all detect events and propose actions and decision making aid to users. However, there is no usage of a common backbone for event detection that can be instantiated, re-used, and reconfigured in different use cases. In this paper, we propose eVM, a generic event Virtual Machine able to detect events in different contexts while allowing domain experts to model and define the targeted events prior to detection. eVM simultaneously considers the various features of the defined events (e.g., temporal, geographical), and uses the latter to detect different featurecentric events (e.g., time-centric, location-centric). eVM is based on different components (an event query language, a query compiler, an event detection core, etc.), but mainly the event detection modules are detailed here. We show that eVM is re-usable in different contexts and that the performance of our prototype is quasi-linear in most cases. Our experimental results showed that the detection accuracy is improved when, besides spatio-temporal information, other features are considered.

show abstract

F-SED: Feature-Centric Social Event Detection

Mansour

Tekli

Arnould

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In the context of social media, existent works offer socialevent-based organization of multimedia objects (e.g., photos, videos) by mainly considering spatio-temporal data, while neglecting other userrelated information (e.g., people, user interests). In this paper we propose an automated, extensible, and incremental Feature-centric Social Event Detection (F-SED) approach, based on Formal Concept Analysis (FCA), to organize shared multimedia objects on social media platforms and sharing applications. F-SED simultaneously considers various event features (e.g., temporal, geographical, social (user related)), and uses the latter to detect different feature-centric events (e.g., user-centric, location-centric). Our experimental results show that detection accuracy is improved when, besides spatio-temporal information, other features, such as social, are considered. We also show that the performance of our prototype is quasi-linear in most cases.

show abstract

Event Detection Refinement Using External Tags for Flickr Collections

Cited by 5 publications

References 9 publications

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

eVM: An Event Virtual Machine Framework

F-SED: Feature-Centric Social Event Detection

Contact Info

Product

Resources

About