Geo-informative discriminative image representation by semi-supervised hierarchical topic modeling

Nowadays, the prevalence of sharing tourist photos to online communities has created an increasing demand for mining discriminative architecture aspects from historic landmarks. Some previous researches have demonstrated that topic models could discover discriminative features represented by meaningful visual-topics. However, they seldom exploited the indicative function of geo-tags and the hierarchy in architecture characteristics. In order to utilize this information, we proposed a semi-supervised hierarchical topic modeling approach (namely, shTM). In our approach, every image could be represented by a probability distribution over selected geo-related visual-topics from a partly randomized topic tree. We evaluated our approach on a real-world dataset with over 26 thousand geo-informative photos from Flickr. Experiments show that shTM topics could reveal more discriminative aspects of a specific architecture than other well-known image features, such as HOG and SIFT, on the tasks of automatic photo categorization and geographical information retrieval.