(Details to be added soon)

Visual Place Categorization

More details coming soon.

Representation for Scene and Place Recognition

We present a representation that is suitable for recognizing the semantic category of natural scenes and indoor environments, e.g. forests, coasts, streets, bedrooms, living rooms, etc.

More details here. C++ source code is available.

Synergistic Activity and Object Recognition

We use RFID and Vision as two complementary sensors in a Bayesian network, and recognize daily activities & objects simultaneously.

Since RFID signals are used as noisy labels, absolutely no manual labeling of images is need (neither for activity segmentations nor for object labels).

More details here.

Fast and Robust Face Detection / Rare Event Detection

We propose FFS to replace the AdaBoost classifier in the Viola-Jones cascade face detector. FFS is about 2 orders of magnitude faster than their original AdaBoost implementation.

The classifiers in a cascade has different learning goals than usual classifiers. We formalize the asymmetric learning goal in a cascade and provides inference algorithm for it.

More details here, C++ source code and a demo program/video included.

Effective and Efficient Sampling Strategies for Imbalanced Learning problem

We propose two methods EasyEnsemble and BalanceCascade to deal with learning problems with imbalanced datasets.

These methods are very efficient – their training time is almost identical to that of the down sampling method (which only use a small portion of the original data set, thus is very fast)

They are also effective – they pick up the information that's thrown away in down sampling by using multiple samples.

See the publication page for more details (paper [J6] and [C5]).

Selective Ensemble of learners – many could be better than all

We show that in a ensemble classifier (e.g. bagging and boosting), it is not always the best to use all the weak classifier in the ensemble. We show that by choosing only a subset of the weak classifiers, the new ensemble will have better accuracy.

See the publication page for more details (paper [J2], [C1] and [C2]).