This document summarizes classical image classification methods, including bag-of-words (BoW) representations and spatial pyramid matching. It discusses extracting local features from images, clustering them into visual words, and representing images as histograms of visual word frequencies. Spatial pyramid matching incorporates spatial information by dividing images into regions and computing histograms for each region. The document also briefly mentions generative topic models and part-based models for image classification.

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

5. Sampling local features from images 05/13/11 Image credits: F-F. Li, E. Nowak, J. Sivic A set of points

8. Bag-of-words (BoW) representation 05/13/11 Adapted from tutorial slides by Fei-Fei et al. Analogy to documents

11. Compute BoW histogram for each image 05/13/11 Assign sift features into clusters BoW histogram representations R1 R2 R3 Compute the frequency of each cluster within an image R1 R2 R3

17. A common pipeline for discriminative image classification using BoW 05/13/11 K-means Dense/Sparse SIFT dictionary Dictionary Learning VQ Coding Dense/Sparse SIFT Spatial Pyramid Pooling Nonlinear SVM Image Classification

18. Combining multiple descriptors 05/13/11 Multiple Feature Detectors Multiple Descriptors: SIFT, shape, color, … VQ Coding and Spatial Pooling Nonlinear SVM Diagram from SurreyUVA_SRKDA, winner team in PASCAL VOC 2008

20. Topic models for images 05/13/11 Latent Dirichlet Allocation (LDA) Fei-Fei et al. ICCV 2005 Slide credit Fei-Fei Li w N c z D  “ beach”

21. Part-based Model 05/13/11 Fischler & Elschlager 1973 Rob Fergus ICCV09 Tutorial

22. For a comprehensive coverage of object categorization models , please visit 05/13/11 Recognizing and Learning Object Categories Li Fei-Fei (Stanford), Rob Fergus (NYU), Antonio Torralba (MIT) http://people.csail.mit.edu/torralba/shortCourseRLOC/