PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

What, Where & How Many? Combining Object Detectors and CRFs
Lubor Ladicky, Paul Sturgess, Karteek Alahari, Chris Russell and Philip Torr
In: European Conference on Computer Vision (ECCV), 5-11 Sep 2010, Crete, Greece.

Abstract

Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent past. The next challenge is to integrate all these algorithms and address the problem of scene understanding. This paper is a step towards this goal. We present a probabilistic framework for reasoning about regions, objects, and their attributes such as object class, location, and spatial extent. Our model is a Conditional Random Field defined on pixels, segments and objects. We define a global energy function for the model, which combines results from sliding window detectors, and low-level pixel-based unary and pairwise relations. One of our primary contributions is to show that this energy function can be solved efficiently. Experimental results show that our model achieves significant improvement over the baseline methods on CamVid and PASCAL VOC datasets.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:6996
Deposited By:Karteek Alahari
Deposited On:09 September 2010