Lecture 15 - Stanford Vision Lab

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Functions for comparing histograms • L1 distance D ( h N 1 , h 2 ) | h1 ( i) h 2 ( i) | i 1 • χ 2 distance D ( h 1 , h 2 ) N h1 ( i) h ( i) h h i 1 1 2 2 ( i) ( i) 2 • Quadratic distance (cross‐bin) D ( h1 , h 2 ) Aij ( h1 ( i) h 2 ( j )) i , j 2 Jan Puzicha, Yossi Rubner, Carlo Tomasi, Joachim M. Buhmann: Empirical Evaluation of Dissimilarity Measures for Color and Texture. ICCV 1999 Fei-Fei Li Lecture 15 - 32 14‐Nov‐11
Learning and Recognition 1. Discriminative method: -NN - SVM 2.Generative method: - graphical models Fei-Fei Li Lecture 15 - 33 14‐Nov‐11
Page 1 and 2: Lecture 15: Object recognition: Bag
Page 3 and 4: What we will learn today? • Bag o
Page 5 and 6: Related works • Early “bag of w
Page 7 and 8: Analogy to documents Of all the sen
Page 9 and 10: definition of “BoW” - Independe
Page 11 and 12: 1.Feature detection and representat
Page 17 and 18: 2. Codewords dictionary formation
Page 19 and 20: 2. Codewords dictionary formation F
Page 21 and 22: Visual vocabularies: Issues • How
Page 23 and 24: 3. Bag of word representation frequ
Page 25 and 26: Learning and Recognition codewords
Page 27 and 28: Discriminative classifiers category
Page 29 and 30: Nearest Neighbors classifier Query
Page 31: K- Nearest Neighbors classifier fro
Page 35 and 36: Support vector machines • Find hy
Page 37 and 38: Nonlinear SVMs •Datasets that are
Page 39 and 40: Nonlinear SVMs • Nonlinear decisi
Page 41 and 42: Pyramid match kernel • Fast appro
Page 43 and 44: What about multi‐class SVMs? •
Page 45 and 46: Object recognition results • ETH
Page 47 and 48: Learning and Recognition 1. Discrim
Page 49 and 50: Some notations • w: a collection
Page 51 and 52: the Naïve Bayes model c w N Graphi
Page 53 and 54: Csurka et al. 2004 Fei-Fei Li Lectu
Page 55 and 56: Generative vs discriminative • Di
Page 57 and 58: What we will learn today? • Bag o
Page 59 and 60: Parts and Structure Literature •
Page 61 and 62: Deformations A B C D Fei-Fei Li Lec
Page 63 and 64: Background clutter Fei-Fei Li Lectu
Page 65 and 66: Learning Models `Manually’ • Ob
Page 67 and 68: So far….. • Representation - Jo
Page 69 and 70: (Semi) Unsupervised learning •Kno
Page 71 and 72: Unsupervised detector training - 2
Page 73 and 74: Learning • Take training images.
Page 75 and 76: Detector Selection •Try out diffe
Page 77 and 78: Learned face model Pre-selected Par
Page 79 and 80: Background images Fei-Fei Li Lectur
Page 81 and 82: Detections of Cars Fei-Fei Li Lectu
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3D Object recognition - Multiple mi
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So far (2)….. • Representation
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Object categorization Fergus et. al
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•Kadir & Brady saliency region de
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Motorbikes Samples from appearance
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Background images evaluated with mo
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Airplanes Fei-Fei Li Lecture 15 - 9
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Summary of results Dataset Fixed sc
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Why this design? • Generic featur
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Supplementary materials • One‐S
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Fei-Fei Li Lecture 15 - 103 14‐No
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Algorithm Burl, et al. Weber, et al
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How do we do better than what stati
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Bayesian framework P(object | test,
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Bayesian framework P(object | test,
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model () space Model Structure Each
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Variational EM Random initializatio
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Faces Motorbikes Airplanes Spotted
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Experiments: obtaining priors airpl
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Number of training examples Fei-Fei
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Number of training examples Fei-Fei
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Lecture 15 - Stanford Vision Lab

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