Section 1: Classical Image Classification Methods

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Framework of Part 2. 2/21/2012. 2. Neighborhood Features, Sampling, Visual WordsDiscriminative Methods Bag-of-Words (BoW) representation Spatial pyramid coordinating (SPM)Generative Methods Part-based strategies Topic models. Framework of Part 2. 2/21/2012. 3. Nearby Features, Sampling, Visual WordsDiscriminative Methods Bag-of-Words (BoW) representation Spatial pyramid coordinating (SPM)Generative Methods Part-based me

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Section 1: Classical Image Classification Methods Kai Yu Dept. of Media Analytics NEC Laboratories America Andrew Ng Computer Science Dept. Stanford University

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Outline of Part 2 Local Features, Sampling, Visual Words Discriminative Methods Bag-of-Words (BoW) portrayal Spatial pyramid coordinating (SPM) Generative Methods P workmanship based strategies Topic models

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Outline of Part 2 Local Features, Sampling, Visual Words Discriminative Methods Bag-of-Words (BoW) portrayal Spatial pyramid coordinating (SPM) Generative Methods P craftsmanship based techniques Topic models

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Local components Distinctive descriptors of nearby picture patches Invariant to neighborhood interpretation, scale, … and in some cases revolution or general relative changes The most celebrated decision is the SIFT highlight

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Sampling nearby elements from pictures An arrangement of focuses Image credits: F-F. Li, E. Nowak, J. Sivic

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Visual words Similar focuses are assembled into one visual word Algorithms: k-implies, agglomerative bunching, … Points from various pictures are then more effectively thought about. Slide credit: Kristen Grauman

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Outline of Part 2 Local Features, Sampling, Visual Words, … Discriminative Methods Bag-of-Words (BoW) portrayal Spatial pyramid coordinating (SPM) Generative Methods P workmanship based strategies Topic models

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Bag-of-words (BoW) portrayal Analogy to records Adapted from instructional exercise slides by Fei-Fei et al.

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BoW for protest order Works entirely well for entire picture grouping Csurka et al. (2004), Willamowski et al. (2005), Grauman & Darrell (2005), Sivic et al. (2003, 2005) Slide credit: Svetlana Lazebnik

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Unsupervised Dictionary Learning SIFT space R1 R2 R3 picture database Sample nearby components from pictures Run k-mean or other bunching calculation to get lexicon Dictionary is likewise called "codebook"

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Compute BoW histogram for each picture R1 R2 Assign filter highlights into groups R3 Compute the recurrence of each bunch inside a picture BoW histogram portrayals

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Indication of BoW histogram Summarize whole picture in view of its circulation of visual word events Turn packs of various sizes into a settled length vector Analogous to sack of words portrayal generally utilized for content classification.

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Image characterization in view of BoW histogram BoW histogram vector space fowl Decision limit puppy Learn a grouping model to decide the choice limit Nonlinear SVMs are generally connected.

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Issues Sampling methodology Learning codebook: measure? directed?, … Classification: which technique? adaptability? Adaptability: how to deal with a large number of information? How to utilize spatial data?

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Spatial data The BoW expels spatial design. This builds the invariance to scale, interpretation, and twisting, B ut penances discriminative power, particularly when the spatial format is essential . Slide adjusted from Bill Freeman

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Spatial pyramid coordinating Compute BoW for picture districts at various areas in different scales Figure credit: Svetlana Lazebnik

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A typical pipeline for discriminative picture grouping utilizing BoW Dictionary Learning Image Classification Dense/Sparse SIFT VQ Coding Dense/Sparse SIFT Spatial Pyramid Pooling K-implies lexicon Nonlinear SVM

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Combining numerous descriptors Multiple Feature Detectors Multiple Descriptors: SIFT, shape, shading, … VQ Coding and Spatial Pooling Nonlinear SVM Diagram from SurreyUVA_SRKDA, champ group in PASCAL VOC 2008

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Outline of Part 2 Local Features, Sampling, Visual Words, … Discriminative Methods Bag-of-Words (BoW) portrayal Spatial pyramid coordinating (SPM) Generative Methods P workmanship based strategies Topic models

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"shoreline"  z c w N D Topic models for pictures Latent Dirichlet Allocation (LDA) Fei-Fei et al. ICCV 2005 Slide credit Fei-Fei Li

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Part-based Model Rob Fergus ICCV09 Tutorial Fischler & Elschlager 1973

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For an extensive scope of question classification models , please visit Recognizing and Learning Object Categories Li Fei-Fei (Stanford), Rob Fergus (NYU), Antonio Torralba (MIT) http://people.csail.mit.edu/torralba/shortCourseRLOC/

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