Pietro Perona

Caltech-256 Object Category Dataset

We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, do...

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

Object class recognition by unsupervised scale-invariant learning

We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible const...

A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition

We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images ...

A probabilistic approach to object recognition using local photometry and global geometry

A Discriminative Framework for Modelling Object Classes

Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn...

Microsoft COCO: Common Objects in Context

Quickly Boosting Decision Trees - Pruning Underachieving Features Early

Boosted decision trees are one of the most popular and successful learning techniques used today. While exhibiting fast speeds at test time, relatively slow training makes them ...