Paper Info
Title
Extracting Image Regions by Structured Edge Prediction
Abstract
We present two approaches to extract regions from structured edge detection. While the state-of-the-art algorithm based on globalized probability of boundary (gPb) generates a hierarchical region tree, it entails significant computational load. In this work, we exploit an efficient algorithm for structured edge prediction to extract regions. To generate high quality regions, we develop a novel algorithm to link the structured edge and gPb hierarchical image segmentation framework with steerable filters. The extracted regions are grouped by the proposed hierarchical grouping method to generate object proposals for effective detection and recognition problems. We demonstrate the effectiveness of our region generation for image segmentation on the BSDS500 database, and region generation for object proposals on the PASCAL VOC 2007 benchmark database. Experimental results show that the proposed algorithm achieves the comparable or superior quality to the state-of-the-art methods.
Year
DOI
Venue
2015
10.1109/WACV.2015.146
WACV
Keywords
Field
DocType
region generation,trees (mathematics),hierarchical region tree,image region extraction,pascal voc 2007 benchmark database,gpb hierarchical image segmentation framework,structured edge prediction,feature extraction,edge detection,steerable filters,globalized probability of boundary generation,hierarchical grouping method,region extraction,bsds500 database,probability,image segmentation,detectors,databases
Computer vision,Object detection,Image gradient,Feature detection (computer vision),Pattern recognition,Computer science,Image texture,Range segmentation,Edge detection,Segmentation-based object categorization,Image segmentation,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2472-6737
1
0.36
References 
Authors
24
3
Name
Order
Citations
PageRank
Yi-Ting Chen1114.20
Jimei Yang2108340.68
Yang Ming-Hsuan315303620.69