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Erschienen in:
Hybrid Example-Based Single Image Super-Resolution Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Joint Visual Phrase Detection to Boost Scene Parsing

verfasst von : Keke Tang, Zhe Zhao, Xiaoping Chen

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Scene parsing is a very challenging problem which attracts increasing interests in many fields such as computer vision and robotics. However, occluded or small objects which are difficult to parse are always ignored. To deal with these two problems, we integrate visual phrase into our joint system, which has been proved to have good performance on describing relationships between objects. In this paper, we propose a joint model which integrates scene classification, object and visual phrase detection, as well as scene parsing together. By encoding them into a Conditional Random Field model, all tasks mentioned above could be solved jointly. We evaluate our method on the MSRC-21 dataset. The experimental results demonstrate that our method achieves comparable and on some occasions even superior performance with respect to state-of-the-art joint methods especially when there exist partially occluded or small objects.

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Vorheriges Kapitel Off-the-Shelf CNN Features for Fine-Grained Classification of Vessels in a Maritime Environment
Nächstes Kapitel If We Did Not Have ImageNet: Comparison of Fisher Encodings and Convolutional Neural Networks on Limited Training Data
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Metadaten
Titel
Joint Visual Phrase Detection to Boost Scene Parsing
verfasst von
Keke Tang
Zhe Zhao
Xiaoping Chen
Copyright-Jahr
2015
Buch
Advances in Visual Computing

Print ISBN: 978-3-319-27862-9

Electronic ISBN: 978-3-319-27863-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-27863-6_36

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