Intro to Machine Learning for Visual Computing

Transcription

1 Intr t Machine Learning fr Visual Cmputing Drthea Tanning, Endgame Slides frm Derek Hiem, Peter Barnum CSC320: Intrductin t Visual Cmputing Michael Guerzhy

2 Eamples f Categrizatin in Visin Part r bject detectin E.g., fr each windw: face r nn-face? Scene categrizatin Indr vs. utdr, urban, frest, kitchen, etc. Actin recgnitin Picking up vs. sitting dwn vs. standing Emtin recgnitin Regin classificatin Label piels int different bject/surface categries Bundary classificatin Bundary vs. nn-bundary Etc, etc.

3 Classificatin (Supervised) Machine Learning Using a training set, make a functin that crrectly classifies new images Eample functin that we already saw: return the name f the face that s the clses match in the training set t the input face

4 Image Categrizatin Training Images Training Image Features Training Labels Classifier Training Trained Classifier

5 Image Categrizatin Training Images Training Image Features Training Labels Classifier Training Trained Classifier Test Image Image Features Testing Trained Classifier Predictin Outdr

6 Feature design is paramunt Mst features can be thught f as templates, histgrams (cunts), r cmbinatins Sample features: The intensities f all the piels in the image (simplest) The average intensity in the image The eigenspace prjectin cefficients Histgrams f Gradients (will talk abut them later)

7 Classifier A classifier maps frm the feature space t a label 2 1

8 Different types f classificatin Eemplar-based: transfer categry labels frm eamples with mst similar features What similarity functin? What parameters? Linear classifier: cnfidence in psitive label is a weighted sum f features What are the weights? Nn-linear classifier: predictins based n mre cmple functin f features What frm des the classifier take? Parameters? Generative classifier: assign t the label that best eplains the features (makes features mst likely) What is the prbability functin and its parameters? Nte: Yu can always fully design the classifier by hand, but usually this is t difficult. Typical slutin: learn frm training eamples.

9 One way t think abut it Training labels dictate that tw eamples are the same r different, in sme sense Features and distance measures define visual similarity Gal f training is t learn feature weights r distance measures s that visual similarity predicts label similarity We want the simplest functin that is cnfidently crrect

10 Eemplar-based Mdels Transfer the label(s) f the mst similar training eamples

11 K-nearest neighbr classifier

12 1-nearest neighbr

13 3-nearest neighbr

14 5-nearest neighbr

15 K-nearest neighbr

16 Using K-NN Simple, a gd ne t try first Higher K gives smther functins N training time (unless yu want t learn a distance functin) With infinite eamples, 1-NN prvably has very lw errr

17 Discriminative classifiers Learn a simple functin f the input features that cnfidently predicts the true labels n the training set y = f Gals 1. Accurate classificatin f training data 2. Crrect classificatins are cnfident 3. Classificatin functin is simple

18 Classifiers: Linear Regressin 2 > a1+b? 2 1

19 Other classifiers Supprt Vectr Machines (SVM) Very ppular Neural Netwrks Always ppular arund UfT, currently the best classifiers arund fr many tasks Decisin Trees Ppular ff-the-shelf classifiers fr new prblems yu encunter T sme etent, all f thse can be used as black bes

20 Eample Image Feature: Histgram f Gradients (HG) Orientatin: 9 bins (fr unsigned angles) Histgrams in 88 piel cells

21 HG Cmpute the HG in each neighburhd in the image, stack all the HG tgether. Nw classify thse vectrs # rientatins X= # features = = 3780 # cells # nrmalizatins by neighbring cells

22 (Find the w and b with Machine Learning magic) pedestrian

23 Cmputer Visin and Pattern Recgnitin, (CVPR) 2005