Image Hierarchical Representations Models based on Latent Dirichlet Allocation

Existing image layer representations methods are very feed-forward, and then not able to deal with small ambiguities. A probabilistic model is proposed, and it learns and deduces each layer in that hierarchy together. Therefore, we consider a recursive probabilistic decomposition process, and derive a new yielded method based on recursive Latent Dirichlet Allocation. We show 2 significant properties of the novel probabilistic method: 1) pulsing another hierarchical to represent the enhanced results on that smooth method; 2) an entire Bayesian method beats a feed-forward running of the novel method. The method can be evaluated on a criterion recognition dataset. It takes the probability of recursive decomposition process into account, and obtains multilayer structure pyramid LDA derived model through the derivation. Experiments demonstrate that the novel technique beats existing hierarchical approaches, and present better performance.