Image Dating, a Case Study to Evaluate the Inter-Battery Topic Model

Abstract: The Inter-Battery Topic Model (IBTM) is an extension of the well known Latent Dirichlet Allocation (LDA) topic model. It gives a factorized representation of multimodal (in this case two views) data, which better separates variation in observed data that is present in both views from variation that is present only in one of the separate views. This thesis is an evaluation and application study of this model with the aim of showing how it can be used in the very difficult classification task of dating grayscale face portraits from a dataset collected from highschool yearbooks. This task has very high intra-class variation and low inter-class variation which calls for techniques to extract the necessary information. An online-trained model is also implemented and evaluated as well as a simplification of the model more suited for this data specifically. The results show improved performance over LDA showing that the factorizing property of IBTM has a positive effect on performance for this type of classification task.