Learning Dynamic Hierarchical Models for Anytime Scene Labeling

With increasing demand for efficient image and video analysis, test-time cost of scene parsing becomes critical for many large-scale or time-sensitive vision applications. We propose a dynamic hierarchical model for anytime scene labeling that allows us to achieve flexible trade-offs between efficie...

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Bibliographic Details
Published in:arXiv.org 2016-08
Main Authors: Liu, Buyu, He, Xuming
Format: Article
Language:English
Subjects:
Cost analysis
Image segmentation
Iterative methods
Labeling
Learning
Markov analysis
Markov chains
Online Access:Get full text
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Summary:With increasing demand for efficient image and video analysis, test-time cost of scene parsing becomes critical for many large-scale or time-sensitive vision applications. We propose a dynamic hierarchical model for anytime scene labeling that allows us to achieve flexible trade-offs between efficiency and accuracy in pixel-level prediction. In particular, our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget by learning a sequence of image-adaptive hierarchical models. We formulate this anytime representation learning as a Markov Decision Process with a discrete-continuous state-action space. A high-quality policy of feature and model selection is learned based on an approximate policy iteration method with action proposal mechanism. We demonstrate the advantages of our dynamic non-myopic anytime scene parsing on three semantic segmentation datasets, which achieves \(90\%\) of the state-of-the-art performances by using \(15\%\) of their overall costs.
ISSN:2331-8422