The Role of Context for Object Detection and Semantic Segmentation in the Wild

In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it conta...

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Bibliographic Details
Published in:2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 891 - 898
Main Authors: Mottaghi, Roozbeh, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Fidler, Sanja, Urtasun, Raquel, Yuille, Alan
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2014
Subjects:
Buildings
Categories
Communities
Computer vision
Conferences
Context
Context modeling
Image segmentation
Object detection
Pascal (programming language)
Pattern recognition
Segmentation
Semantics
State of the art
Vegetation
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of existing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.
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
DOI:10.1109/CVPR.2014.119