Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables

Authors

Khurram Hameed, Edith Cowan UniversityFollow
Douglas Chai, Edith Cowan UniversityFollow
Alexander Rassau, Edith Cowan UniversityFollow

Document Type

Journal Article

Publication Title

Information Processing in Agriculture

Volume

10

Issue

1

First Page

85

Last Page

105

Publisher

Elsevier

School

School of Engineering

RAS ID

42738

Funders

Edith Cowan University (ECU) / Australia and Higher Education Commission (HEC) Pakistan / The Islamia University of Bahawalpur (IUB) Pakistan (5-1/HRD/UESTPI(Batch-V)/1182/2017/HEC)

Comments

Hameed, K., Chai, D., & Rassau, A. (2023). Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables. Information Processing in Agriculture, 10(1), 85-105. https://doi.org/10.1016/j.inpa.2021.09.003

Abstract

The capability of Convolutional Neural Networks (CNNs) for sparse representation has significant application to complex tasks like Representation Learning (RL). However, labelled datasets of sufficient size for learning this representation are not easily obtainable. The unsupervised learning capability of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) provide a promising solution to this issue through their capacity to learn representations for novel data samples and classification tasks. In this research, a texture-based latent space disentanglement technique is proposed to enhance learning of representations for novel data samples. A comparison is performed among different VAEs and GANs with the proposed approach for synthesis of new data samples. Two different VAE architectures are considered, a single layer dense VAE and a convolution based VAE, to compare the effectiveness of different architectures for learning of the representations. The GANs are selected based on the distance metric for disjoint distribution divergence estimation of complex representation learning tasks. The proposed texture-based disentanglement has been shown to provide a significant improvement for disentangling the process of representation learning by conditioning the random noise and synthesising texture rich images of fruit and vegetables.

DOI

10.1016/j.inpa.2021.09.003

Related Publications

Hameed, K. (2022). Computer vision based classification of fruits and vegetables for self-checkout at supermarkets. https://ro.ecu.edu.au/theses/2519

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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.