lecture20 - EECS 442 – Computer vision Object Recognition...

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Unformatted text preview: EECS 442 – Computer vision Object Recognition • Intro • Recognition of 3D objects • Recognition of object categories: • Bag of world models • Part based models • 3D object categorization • Faces Challenges: intra-class variation Usual Challenges: Variability due to: • View point • Illumination • Occlusions Object categorization: Object categorization: the statistical viewpoint the statistical viewpoint ) | ( image zebra p ) ( e zebra|imag no p vs. • Bayes rule: ) | ( ) | ( image zebra no p image zebra p Object categorization: Object categorization: the statistical viewpoint the statistical viewpoint ) ( ) ( ) | ( ) | ( ) | ( ) | ( zebra no p zebra p zebra no image p zebra image p image zebra no p image zebra p ⋅ = posterior ratio likelihood ratio prior ratio • Discriminative methods model posterior • Generative methods model likelihood and prior Discriminative • Direct modeling of Zebra Non-zebra Decision boundary ) | ( ) | ( image zebra no p image zebra p Generative ) | ( zebra image p ) | ( zebra no image p Low Middle High Middle Æ Low ) | ( zebra no image p ) | ( zebra image p – machine learning useful to model intraclass variability Learning – machine learning useful to model intra- class variability – What are you maximizing? Likelihood (Gen.) or performances on train/validation set (Disc.) Learning 0.2 0.4 0.6 0.8 1 1 2 3 4 5 class densities p ( x | C 1 ) p ( x | C 2 ) x 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 posterior probabilities x p ( C 1 | x ) p ( C 2 | x ) generative discriminative – machine learning useful to model intra- class variability • What are you maximizing? Likelihood (Gen.) or performances on train/validation set (Disc.) • Level of supervision • Manual segmentation; bounding box; image labels; noisy labels Learning Contains a motorbike • Batch/incremental (on category and image level; user-feedback ) • Training images: • Issue of overfitting • Negative images for discriminative methods • Priors Part 1: Bag-of-words models This segment is based on the tutorial “ Recognizing and Learning Object Categories: Year 2007 ”, by Prof A. Torralba, R. Fergus and F. Li Related works Related works • Early “bag of words” models: mostly texture recognition – Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001; Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003; • Hierarchical Bayesian models for documents (pLSA, LDA, etc.) – Hoffman 1999; Blei, Ng & Jordan, 2004; Teh, Jordan, Beal & Blei, 2004 • Object categorization – Csurka, Bray, Dance & Fan, 2004; Sivic, Russell, Efros, Freeman & Zisserman, 2005; Sudderth, Torralba, Freeman & Willsky, 2005; • Natural scene categorization – Vogel & Schiele, 2004; Fei-Fei & Perona, 2005; Bosch, Zisserman & Munoz, 2006 Object Object Bag of Bag of ‘ ‘ words words ’ ’ Analogy to documents Analogy to documents Of all the sensory impressions proceeding to the brain, the visual experiences are the...
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This note was uploaded on 10/26/2010 for the course EECS 442 taught by Professor Savarese during the Fall '09 term at University of Michigan.

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lecture20 - EECS 442 – Computer vision Object Recognition...

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