Social Image Analysis Paper

Personalized travel sequence recommendation is from both travelogues and community contributed photos and the heterogeneous meta-data (e.g., tags, geo-location, and date taken) associated with these images[1]. Availability of cheap location sensor for geo-tagging of images on social media has made geo-tagged images very popular. The approaches which are existing usually mine geographical characteristics using a subset of multiple types of image contents or combining those contents linearly, which ignore correlations between different types of contents, and their geographical distributions.
In this paper we studied a novel method to discover geographical characteristics of geo-tagged social images using a geographical topic model called geographical topic model of social images (GTMSIs)[2].This model integrates multiple types of social image contents also the geographical distributions, in which image topics are modeled based
It is very common that text, visual content, and GPS record exist simultaneously on the same social image. Incorporating this rich information may potentially help us to discover the latent information to capture the geographical characteristics of image content. However, this pursuit is nontrivial. It needs to incorporate different type of contents simultaneously using a multi modal model.
2) Visual content and textual description are correlated with each other, and the correlation is different across different regions. Thus, it is reasonable to use middle-level feature, i.e., topic, to capture the correlation between visual content and textual description and model the geographical distribution of the correlation.
3) In reality, there are also many images that are not geo-tagged or do not have any tags. Thus, it needs to analyze these multiple types of image contents and their correlation to support these applications, such as image location prediction and automatic image