mi-DS: Multiple-Instance Learning Algorithm

2013 ◽  
Vol 43 (1) ◽  
pp. 143-154 ◽  
Author(s):  
Dat T. Nguyen ◽  
Cao D. Nguyen ◽  
Rosalyn Hargraves ◽  
Lukasz A. Kurgan ◽  
Krzysztof J. Cios
Keyword(s):  
Learning Algorithm ◽  
Multiple Instance Learning

Robust objectness tracking with weighted multiple instance learning algorithm

2018 ◽  
Vol 288 ◽  
pp. 43-53 ◽  
Author(s):  
Honghong Yang ◽  
Shiru Qu ◽  
Fumin Zhu ◽  
Zunxin Zheng
Keyword(s):  
Learning Algorithm ◽  
Multiple Instance Learning

scholarly journals Patch Based Multiple Instance Learning Algorithm for Object Tracking

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenjie Wang ◽  
Lijia Wang ◽  
Hua Zhang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Partial Occlusion ◽  
Learning Rates ◽  
Classification Score ◽  
Object Based ◽  
Illumination Changes

To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers’ parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.

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

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 832 ◽  
Author(s):  
Keli Hu ◽  
Wei He ◽  
Jun Ye ◽  
Liping Zhao ◽  
Hua Peng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Training Sample ◽  
Multiple Instance Learning ◽  
Superior Performance ◽  
Video Sequences ◽  
Neutrosophic Set ◽  
Object A ◽  
Object Properties ◽  
Shape Distance ◽  
Object Area

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.

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