Abstract | ||
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Human-nameable visual attributes offer many advantages when used as mid-level features for object recognition, but existing techniques to gather relevant attributes can be inefficient (costing substantial effort or expertise) and/or insufficient (descriptive properties need not be discriminative). We introduce an approach to define a vocabulary of attributes that is both human understandable and discriminative. The system takes object/scene-labeled images as input, and returns as output a set of attributes elicited from human annotators that distinguish the categories of interest. To ensure a compact vocabulary and efficient use of annotators' effort, we 1) show how to actively augment the vocabulary such that new attributes resolve inter-class confusions, and 2) propose a novel “nameability” manifold that prioritizes candidate attributes by their likelihood of being associated with a nameable property. We demonstrate the approach with multiple datasets, and show its clear advantages over baselines that lack a nameability model or rely on a list of expert-provided attributes. |
Year | DOI | Venue |
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2011 | 10.1109/CVPR.2011.5995451 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
object recognition,substantial effort,expert-provided attribute,nameability model,human-nameable visual attribute,discriminative vocabulary,efficient use,clear advantage,descriptive property,human annotators,compact vocabulary,nameable attribute,visualization,manifolds,support vector machines | Computer vision,Pattern recognition,Computer science,Baseline (configuration management),Artificial intelligence,Activity-based costing,Vocabulary,Discriminative model,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4577-0394-2 | 118 | 4.21 |
References | Authors | |
25 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Devi Parikh | 1 | 2929 | 132.01 |
Kristen Grauman | 2 | 6258 | 326.34 |