Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

Abstract

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments for low-level applications on BSDS, PASCAL Context, PASCAL Segmentation, and NYUD to evaluate boundary detection performance, showing that COB provides state-of-the-art contours and region hierarchies in all datasets. We also evaluate COB on high-level tasks when coupled with multiple pipelines for object proposals, semantic contours, semantic segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that COB also improves the results for all tasks.

Accepted by T-PAMI. Extended version of "Convolutional Oriented Boundaries", ECCV 2016 (arXiv:1608.02755). Project page: http://www.vision.ee.ethz.ch/~cvlsegmentation/cob/

References in corpus (6)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Caffe: Convolutional Architecture for Fast Feature Embedding
• What makes for effective detection proposals?
• Learning to Segment Object Candidates
• Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
• Situational Object Boundary Detection

Cited by in corpus (3)

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