bag of features 的一个PPT.pdf

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Bag-of-features models

Overview: Bag-of-features models

Origin 1: Texture recognition

Origin 2: Bag-of-words models

Bags of features for object recognition

Bag of features: outline

1. Feature extraction

2. Learning the visual vocabulary

K-means clustering

From clustering to vector quantization

Slide Number 25

Slide Number 26

Visual vocabularies: Issues

Slide Number 28

Image classification

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 • Feature extraction • Visual vocabularies • Discriminative methods • Nearest-neighbor classification • Distance functions • Support vector machines • Kernels • 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 histogram Universal texton dictionary 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 • Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983) US Presidential Speeches Tag Cloud http://chir.ag/phernalia/preztags/

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