Fruit Ripeness Classification with Few-Shot Learning

Hui Fuang Ng, Jie Jin Lo, Chih Yang Lin, Hung Khoon Tan, Joon Huang Chuah, Kar Hang Leung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Deep learning based image classification systems require large amount of training data and long training time. However, the availability of large annotated image dataset is usually limited and expensive to generate, which limits a vision system to adapt to new task efficiently. In this paper, a few-shot classification framework is proposed which can adapt one fruit ripeness classification system to classify new types of fruits using only a few training samples. The proposed framework adopts the meta-learning paradigm where a base network learns to extract meta-features and few-shot classification tasks from the base classes with abundant training samples and then apply the network to similar task on the novel classes using only a few support samples. Experimental results indicate that the proposed framework is able to achieve over 75% ripeness classification accuracy on various fruits using a little as five samples.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enhancing Research and Innovation through the Fourth Industrial Revolution
EditorsNor Muzlifah Mahyuddin, Nor Rizuan Mat Noor, Harsa Amylia Mat Sakim
PublisherSpringer Science and Business Media Deutschland GmbH
Pages715-720
Number of pages6
ISBN (Print)9789811681288
DOIs
StatePublished - 2022
Event11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021 - Virtual, Online
Duration: 5 Apr 20216 Apr 2021

Publication series

NameLecture Notes in Electrical Engineering
Volume829 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021
CityVirtual, Online
Period5/04/216/04/21

Keywords

  • Classification
  • Deep learning
  • Few-shot learning
  • Fruit ripeness

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