Computer Vision Group University of California Berkeley Visual Grouping and Object Recognition Jitendra Malik * U.C. Berkeley * with S. Belongie, C. Fowlkes, T. Leung, D. Martin, G. Mori, J. Puzicha, J.Shi, X. Ren

Computer Vision Group University of California Berkeley From images/video to objects Labeled sets: tiger, grass etc

Computer Vision Group University of California Berkeley

Computer Vision Group University of California Berkeley

Computer Vision Group University of California Berkeley Consistency A BC A,C are refinements of B A,C are mutual refinements A,B,C represent the same percept Attention accounts for differences Image BGL-birdR-bird grass bush head eye beak far body head eye beak body Perceptual organization forms a tree: Two segmentations are consistent when they can be explained by the same segmentation tree (i.e. they could be derived from a single perceptual organization).

Computer Vision Group University of California Berkeley Outline Finding boundaries Recognizing objects Recognizing actions

Computer Vision Group University of California Berkeley Finding boundaries: Is texture a problem or a solution? imageorientation energy

Computer Vision Group University of California Berkeley Statistically optimal contour detection Use humans to segment a large collection of natural images. Train a classifier for the contour/non-contour classification using orientation energy and texture gradient as features.

Computer Vision Group University of California Berkeley Orientation Energy Gaussian 2nd derivative and its Hilbert pair Can detect combination of bar and edge features [Perona & Malik 90]

Computer Vision Group University of California Berkeley Texture gradient = Chi square distance between texton histograms in half disks across edge i j k Chi-square

Computer Vision Group University of California Berkeley

Computer Vision Group University of California Berkeley

Computer Vision Group University of California Berkeley ROC curve for local boundary detection

Computer Vision Group University of California Berkeley Outline Finding boundaries Recognizing objects Recognizing actions

Computer Vision Group University of California Berkeley Biological Shape D’Arcy Thompson: On Growth and Form, 1917 –studied transformations between shapes of organisms

Computer Vision Group University of California Berkeley Deformable Templates: Related Work Fischler & Elschlager (1973) Grenander et al. (1991) von der Malsburg (1993)

Computer Vision Group University of California Berkeley Matching Framework Find correspondences between points on shape Fast pruning Estimate transformation & measure similarity modeltarget...

Computer Vision Group University of California Berkeley Comparing Pointsets

Computer Vision Group University of California Berkeley Shape Context Count the number of points inside each bin, e.g.: Count = 4 Count = FCompact representation of distribution of points relative to each point

Computer Vision Group University of California Berkeley Shape Context

Computer Vision Group University of California Berkeley Comparing Shape Contexts Compute matching costs using Chi Squared distance: Recover correspondences by solving linear assignment problem with costs C ij [Jonker & Volgenant 1987]

Computer Vision Group University of California Berkeley Matching Framework Find correspondences between points on shape Fast pruning Estimate transformation & measure similarity modeltarget...

Computer Vision Group University of California Berkeley Fast pruning Find best match for the shape context at only a few random points and add up cost

Computer Vision Group University of California Berkeley Matching Framework Find correspondences between points on shape Fast pruning Estimate transformation & measure similarity modeltarget...

Computer Vision Group University of California Berkeley 2D counterpart to cubic spline: Minimizes bending energy: Solve by inverting linear system Can be regularized when data is inexact Thin Plate Spline Model Duchon (1977), Meinguet (1979), Wahba (1991)

Computer Vision Group University of California Berkeley Matching Example modeltarget

Computer Vision Group University of California Berkeley Outlier Test Example

Computer Vision Group University of California Berkeley Object Recognition Experiments Handwritten digits COIL 3D objects (Nayar-Murase) Human body configurations Trademarks

Computer Vision Group University of California Berkeley Terms in Similarity Score Shape Context difference Local Image appearance difference –orientation –gray-level correlation in Gaussian window –… (many more possible) Bending energy

Computer Vision Group University of California Berkeley Handwritten Digit Recognition MNIST : –linear: 12.0% –40 PCA+ quad: 3.3% –1000 RBF +linear: 3.6% –K-NN: 5% –K-NN (deskewed) : 2.4% –K-NN (tangent dist.) : 1.1% –SVM: 1.1% –LeNet 5: 0.95% MNIST (distortions): –LeNet 5: 0.8% –SVM: 0.8% –Boosted LeNet 4: 0.7% MNIST : –K-NN, Shape Context matching: 0.63%

Computer Vision Group University of California Berkeley

Computer Vision Group University of California Berkeley COIL Object Database

Computer Vision Group University of California Berkeley Prototypes Selected for 2 Categories Details in Belongie, Malik & Puzicha (NIPS2000)

Computer Vision Group University of California Berkeley Error vs. Number of Views

Computer Vision Group University of California Berkeley Human body configurations

Computer Vision Group University of California Berkeley Deformable Matching Kinematic chain-based deformation model Use iterations of correspondence and deformation Keypoints on exemplars are deformed to locations on query image

Computer Vision Group University of California Berkeley Results

Computer Vision Group University of California Berkeley Trademark Similarity

Computer Vision Group University of California Berkeley Recognizing objects in scenes

Computer Vision Group University of California Berkeley Outline Finding boundaries Recognizing objects Recognizing actions

Computer Vision Group University of California Berkeley Examples of Actions Movement and posture change –run, walk, crawl, jump, hop, swim, skate, sit, stand, kneel, lie, dance (various), … Object manipulation –pick, carry, hold, lift, throw, catch, push, pull, write, type, touch, hit, press, stroke, shake, stir, turn, eat, drink, cut, stab, kick, point, drive, bike, insert, extract, juggle, play musical instrument (various)… Conversational gesture –point, … Sign Language

Computer Vision Group University of California Berkeley Key cues for action recognition “Morpho-kinesics” of action (shape and movement of the body) Identity of the object/s Activity context

Computer Vision Group University of California Berkeley Image/Video  Stick figure  Action Stick figures can be specified in a variety of ways or at various resolutions (deg of freedom) –2D joint positions –3D joint positions –Joint angles Complete representation Evidence that it is effectively computable

Computer Vision Group University of California Berkeley Tracking by Repeated Finding

Computer Vision Group University of California Berkeley Achievable goals in 3 years Reasonable competence at object recognition at crude category level (~1000) Detection/Tracking of humans as kinematic chains, assuming adequate resolution. Recognition of ~ actions and compositions thereof.