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Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

Weiming Hu, Jin Gao, Junliang Xing

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 16, 2016

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This study introduces a novel tensor-based graph embedding algorithm for robust visual object tracking. The method effectively adapts to changing object appearances, improving tracking accuracy and reliability.

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Object tracking requires adaptable appearance models to handle changes in object appearance.
Traditional methods often struggle with variations in object appearance over time.

Purpose of the Study:

To develop a robust visual object tracking algorithm using tensor-based graph embedding.
To create an appearance model adaptable to dynamic changes in object appearance.
To improve tracking accuracy by preserving local geometrical structure and discriminant information.

Main Methods:

Representing image patches as two-order tensors to preserve image structure.
Designing graphs to characterize local geometrical structure of object and background tensor samples.

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Employing graph embedding for tensor dimension reduction while preserving graph structure.

Constructing a discriminant embedding space using transformation matrices.

Implementing a transfer-learning-based semi-supervised strategy for iterative embedding space adjustment.

Integrating the algorithm into a particle filter for optimal object state estimation.

Main Results:

The proposed semi-supervised tensor-based graph embedding learning algorithm demonstrates effectiveness in visual tracking.
Experimental results on the CVPR 2013 benchmark dataset validate the algorithm's performance.
The tracking algorithm successfully captures object appearance characteristics during tracking.

Conclusions:

The developed tensor-based graph embedding approach offers a robust solution for visual object tracking.
The semi-supervised strategy enhances the discriminative power of the embedding space.
The algorithm's adaptability to appearance changes leads to improved tracking performance.