Paper Info
Title
Multiple foreground recognition and cosegmentation: An object-oriented CRF model with robust higher-order potentials
Abstract
Localizing, recognizing, and segmenting multiple foreground objects jointly from a general user's photo stream that records a specific event is an important task with many useful applications. As argued in recent Multiple Foreground Cosegmentation (MFC) work by Kim and Xing, this task is very challenging in that it contrasts substantially from the classical cosegmentation problem, and aims to parse a set of realistic event photos but each containing irregularly occurring multiple foregrounds with high appearance and scene configuration variations. Inspired by the impressive advance in scene understanding and object recognition, this paper casts the multiple foreground recognition and cosegmentation (MFRC) problem within a conditional random fields (CRFs) framework in a principled manner. We capitalize centrally on the key objective that MFRC is to segment out and annotate foreground objects or “things” rather than “stuff”. To this end, we exploit a few complementary objectness cues (e.g. contours, object detectors and layout) and propose novel and efficient methods to capture object-level information. Integrating object potentials as soft constraints (e.g. robust higher-order potentials defined over detected object regions) with low-level unary and pairwise terms holistically, we solve the MFRC task with a probabilistic CRF model. The inference for such a CRF model is performed efficiently with graph cut based move making algorithms. With a minimal amount of user annotations on just a few example photos, the proposed approach produces spatially coherent, boundary-aligned segmentation results with correct and consistent object labeling. Experiments on the FlickrMFC dataset justify that our method achieves state-of-the-art performance.
Year
DOI
Venue
2014
10.1109/WACV.2014.6836062
WACV
Keywords
Field
DocType
move making algorithms,image segmentation,robust higher-order potentials,image recognition,multiple foreground recognition,graph cut,flickrmfc dataset,object-oriented programming,object recognition,graph theory,object-oriented crf model,conditional random fields,object-level information,multiple foreground cosegmentation,robustness,object oriented programming
Conditional random field,Cut,Computer vision,Pattern recognition,Object-oriented programming,Segmentation,Computer science,Image segmentation,Artificial intelligence,Probabilistic logic,CRFS,Cognitive neuroscience of visual object recognition
Conference
ISSN
Citations 
PageRank 
2472-6737
9
0.44
References 
Authors
30
5
Name
Order
Citations
PageRank
Hongyuan Zhu110916.59
Jiangbo Lu2100948.99
jianfei cai31804147.18
jianmin zheng4102499.03
Nadia Magnenat-Thalmann55119659.15