Ontology with Deep Learning for Forest Image Classification
-
Published:2023-04-18
Issue:8
Volume:13
Page:5060
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Kwenda Clopas1ORCID, Gwetu Mandlenkosi1ORCID, Fonou-Dombeu Jean Vincent1ORCID
Affiliation:
1. School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Pietermaritzburg 3209, South Africa
Abstract
Most existing approaches to image classification neglect the concept of semantics, resulting in two major shortcomings. Firstly, categories are treated as independent even when they have a strong semantic overlap. Secondly, the features used to classify images into different categories can be the same. It has been demonstrated that the integration of ontologies and semantic relationships greatly improves image classification accuracy. In this study, a hybrid ontological bagging algorithm and an ensemble technique of convolutional neural network (CNN) models have been developed to improve forest image classification accuracy. The ontological bagging approach learns discriminative weak attributes over multiple learning instances, and the bagging concept is adopted to minimize the error propagation of the classifiers. An ensemble of ResNet50, VGG16, and Xception models is used to generate a set of features for the classifiers trained through an ontology to perform the image classification process. To the authors’ best knowledge, there are no publicly available datasets for forest-type images; hence, the images used in this study were obtained from the internet. Obtained images were put into eight categories, namely: orchards, bare land, grassland, woodland, sea, buildings, shrubs, and logged forest. Each category comprised 100 images for training and 19 images for testing; thus, in total, the dataset contained 800 images for training and 152 images for testing. Our ensemble deep learning approach with an ontology model was successfully used to classify forest images into their respective categories. The classification was based on the semantic relationship between image categories. The experimental results show that our proposed model with ontology outperformed other baseline classifiers without ontology with 96% accuracy and the lowest root-mean-square error (RMSE) of 0.532 compared to 88.8%, 86.2%, 81.6%, 64.5%, and 63.8% accuracy and 1.048, 1.094, 1.530, 1.678, and 2.090 RMSE for support-vector machines, random forest, k-nearest neighbours, Gaussian naive Bayes, and decision trees, respectively.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference40 articles.
1. Xu, N., Wang, J., Qi, G., Huang, T.S., and Lin, W. (2018). Computer Vision: Concepts, Methodologies, Tools, and Applications, IGI Global. 2. Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization;Collin;Percept. Psychophys.,2005 3. Griffin, G., Holub, A., and Perona, P. (2022, September 02). Caltech-256 Object Category Dataset. Available online: https://resolver.caltech.edu/CaltechAUTHORS:CNS-TR-2007-001. 4. Fei-Fei, L., Fergus, R., and Perona, P. (July, January 27). Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA. 5. Shao, M., Li, S., Liu, T., Tao, D., Huang, T.S., and Fu, Y. (2014, January 14–18). Learning relative features through adaptive pooling for image classification. Proceedings of the 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|