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Compact and discriminative visual codebooks are preferred in many visualrecognition tasks. In the literature, a number of works have taken the approachof hierarchically merging visual words of an initial large-sized codebook, butimplemented this approach with different merging criteria. In this work, wepropose a single probabilistic framework to unify these merging criteria, byidentifying two key factors: the function used to model class-conditionaldistribution and the method used to estimate the distribution parameters. Moreimportantly, by adopting new distribution functions and/or parameter estimationmethods, our framework can readily produce a spectrum of novel mergingcriteria. Three of them are specifically focused in this work. In the firstcriterion, we adopt the multinomial distribution with Bayesian method; In thesecond criterion, we integrate Gaussian distribution with maximum likelihoodparameter estimation. In the third criterion, which shows the best mergingperformance, we propose a max-margin-based parameter estimation method andapply it with multinomial distribution. Extensive experimental study isconducted to systematically analyse the performance of the above three criteriaand compare them with existing ones. As demonstrated, the best criterionobtained in our framework achieves the overall best merging performance amongthe comparable merging criteria developed in the literature.

A Generalized Probabilistic Framework for Compact Codebook Creation