Next Article in Journal
PFS: Particle-Filter-Based Superpixel Segmentation
Previous Article in Journal
A Secure, Scalable and Elastic Autonomic Computing Systems Paradigm: Supporting Dynamic Adaptation of Self-* Services from an Autonomic Cloud
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(5), 142; https://doi.org/10.3390/sym10050142

Weakly Supervised Object Co-Localization via Sharing Parts Based on a Joint Bayesian Model

School of Information Engineering, Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 26 February 2018 / Revised: 19 April 2018 / Accepted: 25 April 2018 / Published: 4 May 2018
Full-Text   |   PDF [7185 KB, uploaded 4 May 2018]   |  

Abstract

Objects in images are characterized by intra-class variation, inter-class diversity, and noisy images. These characteristics pose a challenge to object localization. To address this issue, we present a novel joint Bayesian model for weakly-supervised object localization. The differences compared to previous discriminative methods are evaluated in three aspects: (1) We co-localize the similar object per class through transferring shared parts, which are pooling by modeling object, parts and features within and between-class; (2) Labels are given at class level to provide strong supervision for features and corresponding parts; (3) Noisy images are considered by leveraging a constraint on the detection of shared parts. In addition, our methods are evaluated by extensive experiments. The results indicated outperformance of the state-of-the-art approaches with almost 7% and 1.5% improvements in comparison to the previous methods on PASCAL VOC 2007 6 × 2 and Object Discovery datasets, respectively. View Full-Text
Keywords: a joint Bayesian model; weakly supervised object co-localization; shared parts; noisy images a joint Bayesian model; weakly supervised object co-localization; shared parts; noisy images
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wu, L.; Liu, Q. Weakly Supervised Object Co-Localization via Sharing Parts Based on a Joint Bayesian Model. Symmetry 2018, 10, 142.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top