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Object recognition with latent conditional random fields (2005).
| Content Provider | CiteSeerX |
|---|---|
| Author | Collins, Michael Quattoni, Ariadna |
| Abstract | In this thesis we present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modelled as flexible constellations of parts conditioned on local observations. For each object class the probability of a given assignment of parts to local features is modelled by a Conditional Random Field (CRF). We propose an extension of the CRF framework that incorporates hidden variables and combines class conditional CRFs into a unified framework for part-based object recognition. The random field captures spatial coherence between region labels. The parameters of the CRF are estimated in a maximum likelihood framework and recognition proceeds by finding the most likely class under our model. The main advantage of the proposed CRF framework is that it allows us to relax the assumption of conditional independence of the observed data (i.e. local features) often used in generative approaches, an assumption that might be too restrictive for a considerable number of object classes. In the second part of this work we extend the detection model and develop a discriminative |
| File Format | |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Latent Conditional Random Field Combine Class Conditional Crfs Hidden Variable Crf Framework Conditional Random Field Recognition Proceeds Part-based Object Recognition Region Label Local Feature Object Recognition Flexible Constellation Generative Approach Conditional Independence Second Part Unsegmented Cluttered Scene Detection Model Main Advantage Discriminative Part-based Approach Random Field Local Observation Likely Class Spatial Coherence Unified Framework Object Class Considerable Number Maximum Likelihood Framework |
| Content Type | Text |
| Resource Type | Thesis |
