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Deep Learning on Sparse Manifolds for Faster Object Segmentation.

Jacinto C Nascimento, Gustavo Carneiro

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 15, 2017

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This study introduces a novel method combining deep belief networks and sparse manifold learning for accurate 2D segmentation of non-rigid objects. The approach reduces training and inference complexity, requiring less annotated data for robust results.

Area of Science:

Computer Vision
Machine Learning
Medical Imaging

Background:

Non-rigid object segmentation is challenging due to complex deformations.
Current methods often involve rigid detection followed by non-rigid segmentation, benefiting from low-dimensional representations.
Reducing the dimensionality of the rigid detection space is crucial for efficiency.

Purpose of the Study:

To develop a novel approach for 2D non-rigid object segmentation.
To decrease training and inference complexities in machine learning-based segmentation.
To maintain high segmentation accuracy with reduced data requirements.

Main Methods:

Integration of deep belief networks (DBNs) for robust appearance modeling.
Application of sparse manifold learning to reduce the dimensionality of the rigid detection space.

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Combined DBNs and sparse manifolds for efficient and accurate non-rigid segmentation.

Main Results:

Achieved segmentation accuracy comparable to state-of-the-art methods.
Demonstrated lower search complexity during inference.
Showcased effective training with limited annotated data.

Conclusions:

The proposed combination of DBNs and sparse manifolds offers an efficient solution for non-rigid object segmentation.
This method reduces the need for extensive annotated datasets, making it practical for real-world applications.
The approach is effective for segmenting challenging structures like the heart ventricle and facial features.