tailieunhanh - Discriminative and generative methods for bags of features

Invite you to consult the lecture content "Discriminative and generative methods for bags of features" below. Contents of lectures introduce to you the content: Image classification, discriminative methods, nearest neighbor classifier, classification, support vector machines. Hopefully document content to meet the needs of learning, work effectively. | Discriminative and generative methods for bags of features Zebra Non-zebra Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Image classification Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them? Discriminative methods Learn a decision rule (classifier) assigning bag-of-features representations of images to different classes Zebra Non-zebra Decision boundary Classification Assign input vector to one of two or more classes Any decision rule divides input space into decision regions separated by decision boundaries Nearest Neighbor Classifier Assign label of nearest training data point to each test data point Voronoi partitioning of feature space for two-category 2D and 3D data from Duda et al. Source: D. Lowe For a new point, find the k closest points from training data Labels of the k points “vote” to classify Works well provided there is lots of data and the distance function is good K-Nearest Neighbors k = 5 Source: D. Lowe Functions for comparing histograms L1 distance χ2 distance Quadratic distance (cross-bin) Jan Puzicha, Yossi Rubner, Carlo Tomasi, Joachim M. Buhmann: Empirical Evaluation of Dissimilarity Measures for Color and Texture. ICCV 1999 Earth Mover’s Distance Each image is represented by a signature S consisting of a set of centers {mi } and weights {wi } Centers can be codewords from universal vocabulary, clusters of features in the image, or individual features (in which case quantization is not required) Earth Mover’s Distance has the form where the flows fij are given by the solution of a transportation problem Y. Rubner, C. Tomasi, and L. Guibas: A Metric for Distributions with Applications to Image Databases. ICCV 1998 Linear classifiers Find linear function (hyperplane) to separate positive and negative examples Which hyperplane is best? Support vector machines Find hyperplane that maximizes the margin . | Discriminative and generative methods for bags of features Zebra Non-zebra Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Image classification Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them? Discriminative methods Learn a decision rule (classifier) assigning bag-of-features representations of images to different classes Zebra Non-zebra Decision boundary Classification Assign input vector to one of two or more classes Any decision rule divides input space into decision regions separated by decision boundaries Nearest Neighbor Classifier Assign label of nearest training data point to each test data point Voronoi partitioning of feature space for two-category 2D and 3D data from Duda et al. Source: D. Lowe For a new point, find the k closest points from training data Labels of the k points “vote” to classify Works well provided there is lots of data and the distance .