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A Discriminative Latent Model of Object Classes and Attributes
| Citations: | 81 - 5 self |
BibTeX
@MISC{Wang_adiscriminative,
author = {Yang Wang and Greg Mori},
title = {A Discriminative Latent Model of Object Classes and Attributes},
year = {}
}
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Abstract
Abstract. We present a discriminatively trained model for joint modelling of object class labels (e.g. “person”, “dog”, “chair”, etc.) and their visual attributes (e.g. “has head”, “furry”, “metal”, etc.). We treat attributes of an object as latent variables in our model and capture the correlations among attributes using an undirected graphical model built from training data. The advantage of our model is that it allows us to infer object class labels using the information of both the test image itself and its (latent) attributes. Our model unifies object class prediction and attribute prediction in a principled framework. It is also flexible enough to deal with different performance measurements. Our experimental results provide quantitative evidence that attributes can improve object naming. 1
Keyphrases
object class discriminative latent model object class label test image undirected graphical model model unifies visual attribute latent variable quantitative evidence attribute prediction joint modelling object naming trained model different performance measurement experimental result principled framework class prediction
