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Discriminative sequential association latent dirichlet allocation for visual recognition

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Abstract

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.

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Notes

  1. We call it as ‘LDA-50’ for brevity, which is the abbreviation of particular graphical model with given sampling iteration. All below are in the same case.

  2. We use KBoW, LBoW, KSPM and LSPM short for “BoW representation (represent an image as an orderless collection of local features) + RBF kernel SVM”, “BoW representation + linear SVM”, “SPM representation (partition the image into sub-regions and compute histograms of local features found inside each sub-region) + RBF kernel SVM”, “SPM representation + linear SVM” respectively.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grants 61273237, Fundamental Research Funds for the Central Universities 2012HGCX0001 and the National Basic Research Program of China (973 Program) under Grant 2013CB329604.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, P.O. BOX 98, No. 193 Tunxi Road, Hefei, 230009, Anhui, China

    Ting-Ting Yao, Zhao Xie, Jun Gao & Chi Wang

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  1. Ting-Ting Yao
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  2. Zhao Xie
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  3. Jun Gao
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  4. Chi Wang
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Correspondence to Zhao Xie.

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Yao, TT., Xie, Z., Gao, J. et al. Discriminative sequential association latent dirichlet allocation for visual recognition. Pattern Anal Applic 19, 719–730 (2016). https://doi.org/10.1007/s10044-014-0444-0

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