With the explosion of the number of images in personal and on-line collections, efficient techniques for navigating, indexing, labelling and searching images become more and more important. In several studies, the representation of images by topic models in its various aspects and extend the current models. This paper aims to present a brief survey on knowledge based topic model for Unsupervised Object Discovery and Localization techniques in which the goal is to maximize the amount of work needed to re-optimize the solution when the object changes. Number of relative studies namely Latent Dirichlet allocation (LDA) with Multi-Domain Knowledge (MDK), Collaborative randomized search algorithm, Conditional random field and LDA with mixture of Dirichlet trees algorithms are discussed and evaluate the accuracy performance on the several datasets. Comparing to these algorithms the LDA with mixture of tree technique methods having better performance than other methods.

Object discovery, object localization, topic model, and latentDirichlet allocation

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