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Texture classification for visual data using transfer learning.

Vinat Goyal¹, Sanjeev Sharma¹

¹Indian Institute of Information Technology, Pune, India.

Multimedia Tools and Applications

|December 19, 2022

Summary

This summary is machine-generated.

This study enhances texture classification using transfer learning with MobileNetV3 and InceptionV3 models. The models achieved high accuracy on diverse datasets, improving image recognition capabilities.

Keywords:

Computer vision Deep learning InceptionV3 MobileNetV3 Texture classification Transfer learning

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Area of Science:

Computer Vision
Machine Learning

Background:

Texture analysis is fundamental for image recognition and computer vision tasks.
Applications include satellite imagery, forestry, and medical imaging.
Existing texture classification methods have limitations.

Purpose of the Study:

To develop advanced texture classification models.
To outperform existing texture classification methods.
To leverage transfer learning for improved performance.

Main Methods:

Transfer learning approach applied.
Utilized pre-trained models: MobileNetV3 and InceptionV3.
Evaluated on Brodatz, Kylberg, and Outex texture datasets.

Main Results:

Achieved excellent classification accuracy across all datasets.
Kylberg dataset: 100% and 99.89% accuracy.
Brodatz dataset: 99.83% and 99.94% accuracy.
Outex datasets: 99.48% and 99.48% accuracy.

Conclusions:

The proposed models demonstrate superior performance in texture classification.
Transfer learning effectively enhances texture recognition.
The models successfully output texture labels for images.