Next Article in Journal
Modified MHD Radiative Mixed Convective Nanofluid Flow Model with Consideration of the Impact of Freezing Temperature and Molecular Diameter
Next Article in Special Issue
A Hybrid Plithogenic Decision-Making Approach with Quality Function Deployment for Selecting Supply Chain Sustainability Metrics
Previous Article in Journal
Total Weak Roman Domination in Graphs
Previous Article in Special Issue
Neutrosophic Triangular Norms and Their Derived Residuated Lattices
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle

Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation

1
Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China
2
Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
3
College of Information Science and Engineering, Jishou University, Jishou 416000, China
4
College of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(6), 832; https://doi.org/10.3390/sym11060832
Received: 30 May 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 25 June 2019
  |  
PDF [6435 KB, uploaded 25 June 2019]
  |  

Abstract

An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters. View Full-Text
Keywords: visual tracking; neutrosophic weight; objectness; weighted multiple instance learning visual tracking; neutrosophic weight; objectness; weighted multiple instance learning
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

Hu, K.; He, W.; Ye, J.; Zhao, L.; Peng, H.; Pi, J. Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation. Symmetry 2019, 11, 832.

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