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Deep Neural Networks for Image-Based Dietary Assessment

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A novel approach to texture recognition combining deep learning orthogonal convolution with regional input features.

Kar-Seng Loke¹

¹Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan, Taiwan.

Peerj. Computer Science

|April 25, 2024

Summary

Simple convolutional networks outperform deep networks in stationary texture recognition. This study introduces orthogonal convolution and grey level co-occurrence matrix features for improved texture analysis.

Keywords:

Computer vision Convolutional neural networks Glcm Haralick measures Texture features Texture recognition

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Deep convolutional networks (e.g., VGG, ResNet) excel at non-stationary texture analysis due to their ability to detect local microstructures.
Stationary textures, with consistent statistical properties, are poorly detected by current deep learning models.
Existing methods struggle with texture recognition where statistical properties remain constant across image regions.

Purpose of the Study:

To investigate the effectiveness of simpler convolutional networks for stationary texture recognition.
To introduce a novel approach combining orthogonal convolution with grey level co-occurrence matrix (GLCM) features.
To demonstrate superior performance compared to deep learning architectures on stationary texture datasets.

Main Methods:

Development of a seven-layer convolutional network architecture.
Implementation of orthogonal convolution, a novel convolutional technique.
Pre-calculation of regional features using the grey level co-occurrence matrix (GLCM).
Evaluation on the Outex texture dataset.

Main Results:

Achieved an average accuracy improvement of 8.5% in texture recognition.
Demonstrated superior performance of the proposed method over deep networks like GoogleNet, ResNet, VGG, and AlexNet.
Validated the effectiveness of orthogonal convolution and GLCM features for stationary texture analysis.

Conclusions:

Simple convolutional networks, enhanced with orthogonal convolution and GLCM features, surpass deep networks in stationary texture recognition.
The proposed method offers a more efficient and accurate approach for analyzing textures with consistent statistical properties.
This research highlights a promising direction for improving texture analysis in computer vision applications.