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Lecture 15: Object recognition: Bag
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What we will learn today? • Bag o
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Related works • Early “bag of w
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Analogy to documents Of all the sen
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definition of “BoW” - Independe
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1.Feature detection and representat
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1.Feature detection and representat
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1.Feature detection and representat
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2. Codewords dictionary formation
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2. Codewords dictionary formation F
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Visual vocabularies: Issues • How
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3. Bag of word representation frequ
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Learning and Recognition codewords
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Discriminative classifiers category
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Nearest Neighbors classifier Query
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K- Nearest Neighbors classifier fro
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Learning and Recognition 1. Discrim
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Support vector machines • Find hy
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Nonlinear SVMs •Datasets that are
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Nonlinear SVMs • Nonlinear decisi
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Pyramid match kernel • Fast appro
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What about multi‐class SVMs? •
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Object recognition results • ETH
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Learning and Recognition 1. Discrim
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Some notations • w: a collection
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the Naïve Bayes model c w N Graphi
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Csurka et al. 2004 Fei-Fei Li Lectu
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Generative vs discriminative • Di
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What we will learn today? • Bag o
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Parts and Structure Literature •
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Deformations A B C D Fei-Fei Li Lec
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Background clutter Fei-Fei Li Lectu
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Learning Models `Manually’ • Ob
- Page 67 and 68: So far….. • Representation - Jo
- Page 69 and 70: (Semi) Unsupervised learning •Kno
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- Page 77 and 78: Learned face model Pre-selected Par
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- Page 83 and 84: 3D Object recognition - Multiple mi
- Page 85 and 86: So far (2)….. • Representation
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- Page 89 and 90: •Kadir & Brady saliency region de
- Page 91 and 92: Motorbikes Samples from appearance
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- Page 95 and 96: Airplanes Fei-Fei Li Lecture 15 - 9
- Page 97 and 98: Summary of results Dataset Fixed sc
- Page 99 and 100: Why this design? • Generic featur
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- Page 113 and 114: model () space Model Structure Each
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- Page 117: Faces Motorbikes Airplanes Spotted
- Page 121 and 122: Number of training examples Fei-Fei
- Page 123 and 124: Number of training examples Fei-Fei