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Salient Slices: Improved Neural Network Training and Performance with Image Entropy.
Steven J Frank1, Andrea M Frank2
1Art Eye-D Associates LLC steve@art-eye-d.com.
Neural Computation
|May 7, 2020
Summary
This study introduces Salient Slices, a method for analyzing large images with convolutional neural networks (CNNs). By using informative image tiles, this approach improves training and prediction accuracy for high-resolution image classification.
Area of Science:
- Computer Vision
- Machine Learning
- Image Analysis
Background:
- Analyzing large, high-resolution images with convolutional neural networks (CNNs) presents computational challenges.
- Traditional methods may struggle with the scale and complexity of such datasets.
Purpose of the Study:
- To develop an efficient strategy for training and analyzing high-resolution images using CNNs.
- To enhance classification accuracy by focusing on informative image segments.
Main Methods:
- Images are segmented into tiled slices.
- An information criterion, specifically image entropy, is used to select relevant tiles for training and prediction.
- A probability aggregation framework is employed for final predictions based on tile classifications.
Main Results:
- The Salient Slices technique effectively handles large, high-resolution images.
- It provides data augmentation, beneficial for limited image datasets.
- The ensemble approach in prediction enhances overall accuracy.
Conclusions:
- Salient Slices offers a practical and accurate method for CNN-based analysis of high-resolution images.
- The use of image entropy as a criterion ensures tile relevance and aids classification.
- This strategy overcomes limitations of processing unmodified large images.
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