Machine learning & category recognition Cordelia Schmid Jakob Verbeek

This class Part 1: Visual object recognition Part 2 : Machine learning

Visual recognition - Objectives Particular objects and scenes, large databases …

Finding the object despite possibly large changes in scale, viewpoint, lighting and partial occlusion  requires invariant description Viewpoint Scale Lighting Occlusion Difficulties

Very large images collection  need for efficient indexing –Flickr has 2 billion photographs, more than 1 million added daily –Facebook has 15 billion images (~27 million added daily) –Large personal collections –Video collections, i.e., YouTube

Search photos on the web for particular places Find these landmarks...in these images and 1M more Applications

Take a picture of a product or advertisement  find relevant information on the web [Pixee – Milpix]

Applications Finding stolen/missing objects in a large collection …

Applications Copy detection for images and videos Search in 200h of video Query video

10 K. Grauman, B. Leibe Sony Aibo – Robotics –Recognize docking station –Communicate with visual cards –Place recognition –Loop closure in SLAM Slide credit: David Lowe Applications

Instance-level recognition: Approach Extraction of invariant image descriptors Matching descriptors between images -Matching of the query images to all images of a database -Speed-up by efficient indexing structures Geometric verification –Verification of spatial consistency for a short list

This class Lecture 2: Local invariant features –Student presentation: scale and affine invariant interest point detectors

This class Lecture 3: Instance-level recognition: efficient search –Student presentation: scalable recognition with a vocabulary tree

Visual recognition - Objectives Object classes and categories (intra-class variability)

Image classification: assigning label to the image Tasks Car: present Cow: present Bike: not present Horse: not present … Object localization: define the location and the category Car Cow Location Category Visual object recognitionVisual recognition - Objectives

Difficulties: within object variations Variability : Camera position, Illumination,Internal parameters Within-object variations

Difficulties: within-class variations

Visual category recognition Robust image description –Appropriate descriptors for objects and categories Statistical modeling and machine learning for vision –Selection and adaptation of existing techniques

Why machine learning? Early approaches: simple features + handcrafted models Can handle only few images, simples tasks L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph.D. thesis, MIT Department of Electrical Engineering, 1963.

Why machine learning? Early approaches: manual programming of rules Tedious, limited and does not take into accout the data Y. Ohta, T. Kanade, and T. Sakai, “An Analysis System for Scenes Containing objects with Substructures,” International Joint Conference on Pattern Recognition, 1978.

Why machine learning? Today lots of data, complex tasks Internet images, personal photo albums Movies, news, sports

Why machine learning? Today lots of data, complex tasks Surveillance and security Medical and scientific images

Why machine learning? Today: Lots of data, complex tasks Instead of trying to encode rules directly, learn them from examples of inputs and desired outputs

Types of learning problems Supervised –Classification –Regression Unsupervised Semi-supervised Reinforcement learning Active learning ….

Image classification : Approach Excellent results in the presence of background clutter bikesbooksbuildingcarspeoplephonestrees Bag-of-features for image classification

Classification SVM Extract regionsCompute descriptors Find clusters and frequencies Compute distance matrix

Spatial pyramids: perform matching in 2D image space This class Lecture 4: Bag-of-features models for image classification –Student presentation: beyond bags of features: spatial pyramids

Object category localization: examples Car Sofa Bicycle Horse

Object category localization Method with sliding windows (Each window is classified as containing or not the targeted object) Learn a classifier by providing positive and negative examples

Localization approach Histogram of oriented image gradients as image descriptor SVM as classifier, importance weighted descriptors

Localization of “shape” categories Window descriptor + SVMHorse localization

Localization based on shape

This class Lecutre 5: Category-level object localization –Student presentation: object detection with discriminatively trained part based models

This class - schedule Session 1, October –Cordelia Schmid: Introduction –Jakob Verbeek: Introduction Machine Learning Session 2, December –Jakob Verbeek: Clustering with k-means, mixture of Gaussians –Cordelia Schmid: Local invariant features –Student presentation 1 : Scale and affine invariant interest point detectors, Mikolajczyk and Schmid, IJCV Session 3, December –Cordelia Schmid: Instance-level recognition: efficient search –Student presentation 2: Scalable recognition with a vocabulary tree, Nister and Stewenisus, CVPR 2006.

Plan for the course Session 4, December –Jakob Verbeek: Mixture of Gaussians, EM algo.,Fisher Vector image representation –Cordelia Schmid: Bag-of-features models for category-level classification –Student presentation2: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, Lazebnik, Schmid and Ponce, CVPR Session 5, January –Jakob Verbeek: Classification 1: generative and non-parameteric methods –Student presentation 4: Large-scale image retrieval with compressed Fisher vectors, Perronnin, Liu, Sanchez and Poirier, CVPR –Cordelia Schmid: Category level localization: Sliding window and shape model –Student presentation 5: Object detection with discriminatively trained part based methods, McAllester and Ramanan, PAMI This class - schedule

Plan for the course Session 6, January –Jakob Verbeek: Classification 2: discriminative models –Student presentation 6:TagProp: discriminative metric learning in nearest neighbor models for image auto-annotation, Guillaumin, Mensink, Verbeek and Schmid, ICCV –Student presentation 7: IMG2GPS: estimating geographic information from a single image, Hays and Efros, CVPR This class - schedule

This class Class web page at – –Slides available after class Student presentations –20 minutes oral presentation with slides, 5 minutes questions –Two students present together one paper Grades –50% final exam –25% presentation –25% short quiz after each presentation