Abstract
We present an integrated model for Bayesian learning of sparse representation and classifier training, and apply it for the task of visual recognition. Most previous work learns the sparse representation and trains the classifier on top of it in two separate steps. We cast these two into a unified probabilistic model. This way, the supervised labels can effectively affect the learning of the sparse representation. In the training phase, the inference of the joint expectation for dictionary, code, classifier and other variables under the observation of descriptors and labels is carried out by Gibbs Sampling. In the testing phase, based on the learned parameters, the sparse code and the class label of the image are obtained by Bayesian inference. The proposed model is evaluated on Caltech 101 dataset and its efficacy is demonstrated by a careful analysis of the experimental results.
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Li, J., Hua, G., Lan, X., Zheng, N. (2013). An Integrated Model for Bayesian Learning of Sparse Representation and Classifier Training. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_57
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DOI: https://doi.org/10.1007/978-3-319-03731-8_57
Publisher Name: Springer, Cham
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