Topic modeling in multimedia: algorithms and applications

Excerpt

The past decade has seen a rapid development of probabilistic topic models notably probabilistic latent semantic index (PLSI) and latent Dirichlet allocation (LDA). Originally, topic modeling methods have been used to find thematic word clusters called topics from a collection of documents. Since the bag-of-word (BOW) representations have been widely extended to represent both images and videos, topic modeling techniques have found many important applications in the multimedia area. Typical examples include natural scene categorization, human action recognition, multi-label image annotation, part of speech annotation, topic identification and spoken document segmentation. The advantage of topic models lies in their elegant graphical representations and efficient approximate inference algorithms. In the meanwhile, many real-world systems use topic modeling methods to automatically do the feature engineering job. However, different applications require investigating different topic models and inference algorithms to improve the overall performance. Therefore, how to design proper topic models and efficient inference algorithms for specific multimedia applications still remains a challenging problem. At the meantime, multimedia applications often have large-scale data sets, and thus big topic modeling techniques are still an urgent need. …