Approaches to Representing and Recognizing ObjectsWhat the course is aboutThis is very hardSo how do we have a class about this?Fundamental thingsApplication of these ideas to visual classificationClass Goal: Prepare us to solve problems of visual classificationApproaches to Visual ClassificationSlide 9How this might changeRequirements (1)Requirements (2)Requirements (3)Requirement (4)Number of CreditsYour BackgroundFirst HomeworkApproaches to Representing and Recognizing Objects Visual ClassificationCMSC 828J – David JacobsWhat the course is about•Visual Classification–Recognizing nouns and verbs from images.•This is one of the key problems of vision/cognition.–~ half of cerebral cortex is vision.–Vision divides roughly into what and where.•This class is about what.This is very hard•What is a class?–Ill-defined.–Tremendous variability.•And how do we relate images to objects or actions?•Current solutions grossly inadequate.So how do we have a class about this?1. Learn some fundamental things relevant to visual classification.2. See how researchers have tried to apply these to visual classification.Fundamental things•Mostly lectures (but also discussion of some important papers).•Much of it mathematical and computational–Geometry of projection and invariance; PCA; shape spaces and shape matching; wavelet representations of images; stochastic models of classes; learning theory.•But we draw from other fields too:–Philosophy: what is a class?–Biology: how does shape vary in nature?–Psychology: How do people do classification?Application of these ideas to visual classification•Read papers and discuss.•Shows how fundamental ideas can be used.•How math and computation interact.•Don’t solve big problem, but often useful for smaller problems.Class Goal: Prepare us to solve problems of visual classification•Learn fundamental concepts important for vision.•Get us to think about what classification is.•Understand state-of-the-art attempts to solve it.Approaches to Visual Classification•Definitional: a class is defined by the presence or absence of properties (a point in feature space).•A class is a subspace of images.•Class is determined by similarity of images.•Class represented by a generative model.•Classes and generic learning.A tour of the syllabusNote no classes 10/14, 10/16.How this might change•Probably way too much material.•Lectures may be longer than indicated.•May merge classes:–16 & 17 (wavelets).–26 & 27 (linear separators).Requirements (1)•Read papers before they are presented.•Paper reviews.–On classes where papers are presented, by me or students, you must turn in a 1 page review of one paper before class.–One paragraph summarizing main points.•Doing this well is enough for a B.–One paragraph critiquing ideas, suggesting new directions. •Do this well for an A.–20% of grade.Requirements (2)•Presentations–Students will be assigned in pairs to present and lead class discussion of two papers.–I will try to scare you into doing a good job.–Each student goes once (maybe twice).–Sign up for this by next Tuesday.–20% of grade.Requirements (3)•Midterm and Final–Will cover materials in lectures.–40% of grade.Requirement (4)•Project. Choose 1–5 page research proposal. •Extend or build on work discussed in class.•Can focus on approaches you presented.–Programming project and write-up.•Discuss with me first.•Implement some method (eg., winnow, deformable template matching) and try on some data.•Should be like long problem set, not like a big project. No incompletes.Number of Credits•3 credits, do all 4.•2 credits, do 1-3.•1 credit, do 1-2.Your Background•Calculus, linear algebra, probability is essential.•Math that makes you really learn these topics is important.•Other math very helpful: functional analysis (fourier transforms), wavelets, geometry, stochastic processes, optimization.•Knowledge of vision may help a little.First Homework•Readings for Thursday.•Review is due.•Choose papers to present by Tuesday