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Generative learning methods for bags of features

Generative methods, topic discovery in images, application of pLSA action recognition, multiple actions,. As the main contents of the lecture "Generative learning methods for bags of features". Each of your content and references for additional lectures will serve the needs of learning and research. | Generative learning methods for bags of features Model the probability of a bag of features given a class Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Generative methods We will cover two models, both inspired by text document analysis: Naïve Bayes Probabilistic Latent Semantic Analysis The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class fi: ith feature in the image N: number of features in the image The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class wj: jth visual word in the vocabulary M: size of visual vocabulary n(wj): number of features of type wj in the image fi: ith feature in the image N: number of features in the image The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class No. of features of type wj in training images of class c Total no. of features in training images of class c p(wj | c) = The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class No. of features of type wj in training images of class c + 1 Total no. of features in training images of class c + M p(wj | c) = (Laplace smoothing to avoid zero counts) The Naïve Bayes model Csurka et al. 2004 Maximum A Posteriori decision: (you should compute the log of the likelihood instead of the likelihood itself in order to avoid underflow) The Naïve Bayes model Csurka et al. 2004 w N c “Graphical model”: Probabilistic Latent Semantic Analysis T. Hofmann, Probabilistic Latent Semantic Analysis, UAI 1999 zebra grass tree Image = p1 + p2 + p3 “visual topics” Probabilistic Latent Semantic Analysis Unsupervised technique Two-level generative model: a document is a mixture of topics, and each topic has its own characteristic word distribution w d z T. Hofmann, Probabilistic Latent Semantic Analysis, UAI 1999 document | Generative learning methods for bags of features Model the probability of a bag of features given a class Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Generative methods We will cover two models, both inspired by text document analysis: Naïve Bayes Probabilistic Latent Semantic Analysis The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class fi: ith feature in the image N: number of features in the image The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class wj: jth visual word in the vocabulary M: size of visual vocabulary n(wj): number of features of type wj in the image fi: ith feature in the image N: number of features in the image The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class No. of features of type wj in training images of class c Total no. of features in .