Capturing Long-Tail Distributions of Object Subcategories

We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare. We describe distributed algorithms for learning large- mixture models that capture long-tail distributions, which are hard to model with current approaches. We introduce a general...

Full description

Saved in:
Bibliographic Details
Published in2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 915 - 922
Main Authors Xiangxin Zhu, Anguelov, Dragomir, Ramanan, Deva
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Accuracy
Algorithms
Clustering algorithms
Computational modeling
Computer vision
Conferences
Force
Learning
Mathematical models
Optimization
Pattern recognition
Searching
Training
Trains
Visualization
Volatile organic compounds
Online AccessGet full text

Cover

More Information
Summary:We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare. We describe distributed algorithms for learning large- mixture models that capture long-tail distributions, which are hard to model with current approaches. We introduce a generalized notion of mixtures (or subcategories) that allow for examples to be shared across multiple subcategories. We optimize our models with a discriminative clustering algorithm that searches over mixtures in a distributed, "brute-force" fashion. We used our scalable system to train tens of thousands of deformable mixtures for VOC objects. We demonstrate significant performance improvements, particularly for object classes that are characterized by large appearance variation.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
2575-7075
DOI:10.1109/CVPR.2014.122