# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 4 Single-Modality Data Representation and Event Detection
### Visual Based Data Representation and Event Detection
#### 4.2.1 Image Based Data Representation  
Image is also a common form of data for event detection except for text. There has been a lot of research on event detection based on image information. For example, [PERSON] et al. [46] proposed a method to classify different event types based on the photos. They mined the most informative features for the event recognition from photo collections. Dense Scale-Invariant Feature Transform (SIFT) [47] and color features are extracted and represented by the Bag-of-Words (BOW) representation. The most important features are selected by PageRank and Support Vector Machines (SVM) and are used to predict the types of events. [PERSON] et al. [48] proposed a latent sub-event approach for event recognition. They used discriminative hidden Markov to model the transitions between states, which is called the Stopwatch Hidden Markov model (SHMM). Experiments on the model show that their proposed model outperforms approaches based only on feature pooling or a classical hidden Markov model. [PERSON] et al. [49] associated personal image collections with events. They analyzed the photo collection rather than analyzing individual images. They exploited three main features, namely Saliency, GIST and Time to extract an event signature, which is the characteristic for a specific event type. The proposed method detected different event types such as graduation, wedding, or different types of vacations and sports events. Moreover, [PERSON] and [PERSON] [50] proposed a method to cluster the images, which takes into account textual annotations, time and geo-location of the images. They also extended a well-known clustering algorithm called Suffix Tree Clustering (STC), which originally developed to cluster text documents using document snippets. [PERSON] et al. [51] presented a novel image clustering framework scheme that relies on the creation of two image graphs representing and two kinds of similarity between images, with the similarity based on their visual features and their tags. They use visual and tag similarity and perform clustering on such image similarity graphs by means of community detection. Subsequently, they classify the resulting image clusters as landmarks or events by using features related to the temporal, social, and tag characteristics of image clusters. [PERSON] et al. [52] presented a similarity-based method to cluster digital photos, the cluster is based on time and image content.  
[PERSON] et al. [53] proposed a hierarchical model that can be used to recognize events in personal photo collections. The method leverages multiple features such as time, scene and objects. They also study the problem of recognizing events in personal photo albums. An attention network is introduced to learn the representations of photos. Then a hierarchical model is adopted to recognize events from coarse to fine by using multi-granular features [54]. [PERSON] et al. [55] proposed a method to discover events from the Twitter stream. They made use of \"geo-photo tweets\", which are tweets including both geotags and photos. Their proposed method mined various events visually and geographically by using visual information as well as textual information. [PERSON] et al. [56] explored a fully automated approach for the detection of specific social events. Both the text and image metadata of photo collection are used. To explore the applicability of visual-based information, they employed visual models which are subsequently used to assign an image to a cluster. [PERSON] et al. [57] proposed photo-based Mobile Crowdsourcing (MCS) framework for event reporting, which used an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. A summary of the representation of image based event detection is shown in Table 1.