1. Christopher M. Bishop Object Recognition: A Statistical Learning Perspective Microsoft Research, Cambridge Sicily, 2003

2. Object Recognition Workshop, SicilyChristopher M. Bishop Question 1 “Will visual category recognition be solved by an architecture based on classification of feature vectors using advanced learning algorithms?” No –large number of classes –many degrees of freedom of variability (geometric, photometric,...) –transformations are highly non-linear in the pixel values (objects live on non-linear manifolds) –occlusion –expensive to provide detailed labelling of training data

3. Object Recognition Workshop, SicilyChristopher M. Bishop Question 2 “If we want to achieve a human like capacity to recognise 1000s of visual categories, learning from a few examples, what will move us forward most significantly?” Large training sets –algorithms which can effectively utilize lots of unlabelled/partially labelled data But: should the models be generative or discriminative?

4. Object Recognition Workshop, SicilyChristopher M. Bishop Generative vs. Discriminative Models Generative approach: separately model class-conditional densities and priors then evaluate posterior probabilities using Bayes’ theorem Discriminative approaches: 1.model posterior probabilities directly 2.just predict class label (no inference stage)

5. Object Recognition Workshop, SicilyChristopher M. Bishop Generative vs. Discriminative

6. Object Recognition Workshop, SicilyChristopher M. Bishop Advantages of Knowing Posterior Probabilities No re-training if loss matrix changes –inference hard, decision stage is easy Reject option: don’t make decision when largest probability is less than threshold Compensating for skewed class priors Combining models –e.g. independent measurements:

7. Object Recognition Workshop, SicilyChristopher M. Bishop Unlabelled Data Class 1 Class 2 Test point

8. Object Recognition Workshop, SicilyChristopher M. Bishop Unlabelled Data

9. Object Recognition Workshop, SicilyChristopher M. Bishop Generative Methods Relatively straightforward to characterize invariances They can handle partially labelled data  They wastefully model variability which is unimportant for classification  They scale badly with the number of classes and the number of invariant transformations (slow on test data)

10. Object Recognition Workshop, SicilyChristopher M. Bishop Discriminative Methods They use the flexibility of the model in relevant regions of input space They can be extremely fast once trained  They interpolate between training examples, and hence can fail if novel inputs are presented  They don’t easily handle compositionality (e.g. faces can have glasses and/or moutaches and/or hats)

11. Object Recognition Workshop, SicilyChristopher M. Bishop Hybrid Approaches Generatively inspired models, trained discriminatively –state of the art in speech recognition –hidden Markov model handles time-warp invariances –parameters determined by maximum mutual information not maximum likelihood