Limitations of Cotemporary Classification Algorithms

• Major limitations of classification algorithms like Adaboost, SVMs, or Naïve Bayes include,

• Requirement of a large amount of labeled training data.

• Fixed parameters after the end of training phase, i.e., these classifiers can not attune themselves to particular detection scenarios after deployment.

Synopsis

• A boosted classification framework, in which,

• Separate views (features) of the data used for online collection of training examples through co-training.

• Combined view (all features) used to make classification decisions.

• Background modeling used to prune away stationary regions to speed up the classification process.

• Global feature representations used for robustness.

Online Detection and Classification of Moving Objects Using Progressively Improving Detectors

Omar Javed, Saad Ali, Mubarak Shah Computer Vision Lab

University of Central FloridaInitial Training

• The Bayes Classifier is used as the base (weak) classifier for boosting.

• Each feature vector component vq ,where q ranges from 1,.., m1+m2 (for two object classes + background class), is used to learn the pdf for

each class.

• The classification decision by the qth base classifier hq is taken as ci,

• Adaboost.M1 (Freund and Schapire, 96) is used to learn the strong classifier, from the initial training data and the base classifiers.

Results

• Initial Training: 50 examples of each class

• All examples scaled to 30x30 vector

• Validation Set :20 images per class

• Testing on three sequences

(a) Change in performance with increase in time for sequence 1,2 and 3. The performance was measure over two minute intervals. Over 150 to 200 detection decisions were usually made in this time period.

Properties of the Proposed Algorithm

• Requirement of minimal training data

• Automated online selection of training examples after deployment, for continuous improvement.

•. Near real time performance (4-5 frames/sec)

The Online Co-training Framework

• During the test phase, select example for training, if

• more than 10% of the classifiers confidently predict the label of an example.

• Example’s margin is less than the computed thresholds.

• Once an example has been labeled by the co-training mechanism, an online boosting algorithm by [Oza and Russel,02] is used to update the base classifiers and the boosting coefficients.

Feature Extraction

• Features for classification are derived from Principal Component Analysis of the appearance templates of the training examples.

• For each object class ci (excluding background) an appearance subspace, represented by d x mi projection matrix Si, is constructed.

• m chosen such that eigenvectors represent 99% of respective subspace.

• Appearance features for base learners are obtained by projecting a training example ‘r’ into appearance subspace of

each object class.

• for two object classes the feature vector v of an example will be,

1 1 1[ ,..., ] Tmv v r S

1 1 1 2 2[ ,..., ] Tm m mv v r S

Pro

babi

lity

Vehicle

Person

Clutter

Histograms of a feature coefficient from the appearance subspace

Row 1: Top 3 eigenvectors for person appearance subspace. Row 2: Vehicle appearance subspace

( | ) ( | )i q j qP c v P c v i j If

: ( )

1( ) arg max log

ic C i h x c i

H x

Where β {i=1…N} are the boosting parameters, C is the set

of classesThe Online Co-training Framework : Key Observations

• Boosting mechanism selects the least correlated base classifiers.

• Ideal for co-training!

• Examples confidently labeled by one classifier are used to train the other.

• Only the observations lying close to the decision boundary of the boosted classifier are useful for improving classification performance.

• Use examples with small margins for online training.

The Online Co-training Framework: Implementation Steps

• Determine confidence thresholds for each base classifier, using a validation data set.

• For class ci and jth base classifier hj set the confidence threshold, Tj,ci

base ( highest probability achieved by a negative example).

• Compute Margin thresholds Tcibase are from the validation data

set.

Foreground Models

Featu

reExtra

ctio

n

ROIs

Background

Background Models

Updated parameters

Color Classifier

Edge Classifier

Base Classifiers

Boosted Classifier

Classification Output

Updated Boosted Parameters

Co-Training Decision (if confident prediction by one set)

Updated weak learners

Information flow for the real-time object classification system

Performance vs. the number of co-trained examples

for the three sequences. Relatively few examples

are required for improving the detection rates since

these examples are from the same scene in which the

classifier is being evaluated.