We propose a novel approach to learn and recognize natural scene categories. Unlike previous
work, it does not require experts to annotate the training set. We represent the image of …

A new definition of scale-space is suggested, and a class of algorithms used to realize a
diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way …

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, …

A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed.
It consists of two steps: rst forming activation maps on certain feature channels, and then …

We present a new dataset with the goal of advancing the state-of-the-art in object recognition
by placing the question of object recognition in the context of the broader question of scene …

CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird
species. The extended version roughly doubles the number of images per category and adds …

Pedestrian detection is a key problem in computer vision, with several applications that have
the potential to positively impact quality of life. In recent years, the number of approaches to …

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 …

Current computational approaches to learning visual object categories require thousands of
training images, are slow, cannot learn in an incremental manner and cannot incorporate …

Learning visual models of object categories notoriously requires hundreds or thousands of
training examples. We show that it is possible to learn much information about a category …