Abstract
This paper presents a novel topic model named Affine Invariant Topic Model(AITM) for generic object recognition. Abandoning the “bag of words” assumption in traditional topic models, AITM incorporates spatial structure into traditional LDA. AITM extends LDA by modeling visual words with latent affine transformations as well as latent topics, treating topics as different parts of objects and assuming a common affine transformation of visual words given a certain topic. MCMC is employed to make inference for latent variables, MCMC-EM algorithm is used to parameter estimation, and Bayesian decision rule is used to perform classification. Experiments on two challenging data sets demonstrate the efficiency of AITM.
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Li, Z., Zhang, L. (2010). Affine Invariant Topic Model for Generic Object Recognition. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_20
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DOI: https://doi.org/10.1007/978-3-642-13318-3_20
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