tailieunhanh - Bag-of-features models
Overview bag of features models, bags of features for image classification, from clustering to vector quantization, image classification,. As the main contents of the lecture "Bag-of-features models". Each of your content and references for additional lectures will serve the needs of learning and research. | Bag-of-features models Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Overview: Bag-of-features models Origins and motivation Image representation Discriminative methods Nearest-neighbor classification Support vector machines Generative methods Naïve Bayes Probabilistic Latent Semantic Analysis Extensions: incorporating spatial information Origin 1: Texture recognition Texture is characterized by the repetition of basic elements or textons For stochastic textures, it is the identity of the textons, not their spatial arrangement, that matters Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001; Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003 Origin 1: Texture recognition Universal texton dictionary histogram Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001; Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003 Origin 2: Bag-of-words models Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983) Origin 2: Bag-of-words models US Presidential Speeches Tag Cloud Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983) Origin 2: Bag-of-words models US Presidential Speeches Tag Cloud Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983) Origin 2: Bag-of-words models US Presidential Speeches Tag Cloud Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983) Bags of features for image classification Extract features Extract features Learn “visual vocabulary” Bags of features for image classification Extract features Learn “visual vocabulary” Quantize features using visual vocabulary Bags of features for image classification Extract . | Bag-of-features models Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Overview: Bag-of-features models Origins and motivation Image representation Discriminative methods Nearest-neighbor classification Support vector machines Generative methods Naïve Bayes Probabilistic Latent Semantic Analysis Extensions: incorporating spatial information Origin 1: Texture recognition Texture is characterized by the repetition of basic elements or textons For stochastic textures, it is the identity of the textons, not their spatial arrangement, that matters Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001; Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003 Origin 1: Texture recognition Universal texton dictionary histogram Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001; Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003 Origin 2: Bag-of-words .
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