Semi-supervised Multimodal Clustering Algorithm Integrating Label Signals for Social Event Detection

Author(s):  
Zhenguo Yang ◽  
Qing Li ◽  
Zheng Lu ◽  
Yun Ma ◽  
Zhiguo Gong ◽  
...  
Author(s):  
Sheba Selvam ◽  
Ramadoss Balakrishnan ◽  
Balasundaram Sadhu Ramakrishnan

Progression in digital technology and the fame of social media sites such as Facebook, YouTube, Flickr etc., necessitate sharing memories. This results in a colossal amount of multimedia content such as text, audio, photographs and video on the web. Retrieving photographs exclusively from web in the large collection is a challenging task. One way to retrieve photographs is by identifying them as events. The automatic organization of a multimedia collection into groups of items, where each group corresponds to a distinct event is described as Social Event Detection (SED). Contextual information, present for each photograph in social media adds semantics to the photographs. For semantic based retrieval, ontology based approaches yield good retrieval results, by reducing the number of false positives. So, the proposed approach moves with domain ontology construction followed by a hybrid clustering approach. Compared to the existing single-pass incremental clustering algorithm, the proposed approach ensures a good f-measure of 0.8608.


Author(s):  
Georgios Petkos ◽  
Symeon Papadopoulos ◽  
Emmanouil Schinas ◽  
Yiannis Kompatsiaris

2016 ◽  
Vol 20 (5) ◽  
pp. 995-1015 ◽  
Author(s):  
Zhenguo Yang ◽  
Qing Li ◽  
Wenyin Liu ◽  
Yun Ma ◽  
Min Cheng

Author(s):  
Yue Gao ◽  
Sicheng Zhao ◽  
Yang Yang ◽  
Tat-Seng Chua

2018 ◽  
Vol 48 (11) ◽  
pp. 3218-3231 ◽  
Author(s):  
Sicheng Zhao ◽  
Yue Gao ◽  
Guiguang Ding ◽  
Tat-Seng Chua

Sign in / Sign up

Export Citation Format

Share Document