Discriminative sequential association latent dirichlet allocation for visual recognition

Graphical models have been employed in a wide variety of computer vision tasks. Assignments of latent variables in typical models usually suffer the confused explanation in sampling way. In this paper we present discriminative sequential association Latent Dirichlet Allocation, a novel statistical model for the task of visual recognition, and especially focus on the case of few training examples. By introducing the switching variables and formulating the direct discriminative analysis, the sequential associations are considered as priori to establish a relevance determination mechanism to obtain the reasonable assignments of latent variables and avoid the invalid labeling oscillations. We demonstrate the power of our model on two common-used datasets, and the experiment results show that our model can achieve better performances with efficient convergence and give well interpretations of specific topic assignments at the same time.