# Ontology with Deep Learning for Forest Image Classification
## 2 Related Works  
Image classification has received much attention in the fields of computer vision and image processing [11; 12; 13; 14; 15; 16]. A study [11] developed a model that harmonized ontology and HMAX features to perform image classification using merged classifiers. The basic idea behind the model was to exploit ontological relationships that exist between image classes or categories. For better discrimination between classes, the procedure involved training visual feature classifiers and merging outputs of hypernym-hyponym classifiers. The model included three components: (1) feature extraction, (2) ontology building, and (3) image classification. The visual features were obtained from the training dataset, and ontology building was carried out by mainly following the process of concept extraction and relationship generation. Visual features extracted from the training set and the ontology were used to perform image classification using a linear orange support-vector machine (SVM) classifier. In terms of accuracy, the model achieved an accuracy of 0.63, while the baseline method without ontology obtained an accuracy of 0.59. However, as coined by [12],HMAX does not perform very well in terms of feature extraction over a limited dataset. To circumvent this shortcoming, the proposed model in this study has adopted an ensemble of CNNs to generate features for subsequent image classification. Another study [1] proposed an ontological random forest algorithm for forest image classification. The algorithm's basic idea was that the semantic relationships between categories determined the splitting of the decision tree. Multiple-instance learning was used to provide a learning platform for generating hierarchical features that were then used to capture visual dissimilarities at various concept levels. Semantic splitting was used to build decision trees, and semantic relationships were used to learn hierarchical weak features. The experimental results showed that the approach not only outperformed state-of-the-art approaches but was also capable of identifying semantic features at different concept levels. The drawback of this study was that feature generation was hugely dependent on weak attribute learning. To solve this problem, the proposed study used an ensemble deep learning approach to generate all plausible features for subsequent image classification. An algorithm that automatically builds image classification trees was proposed in [17]. A set of categories was recursively divided into two minimally confused subsets and achieved 5-20-fold speedups over other methods. Other authors [18] used lexical semantic networks to integrate knowledge about inter-category relationships into the learning process of visual appearance. A semantic hierarchy of discriminative classifiers was used for object detection. The challenge encountered was that object recognition was marred by the fact that the algorithm did not support weak attribute reasoning. To overcome this challenge, the proposed study incorporated the bagging algorithm because it has the ability to learn weak attributes. A new formalism that incorporated hierarchy and exclusion (HEX) graphs to perform object classification by exploiting the rich structure of real-world labels was introduced [19]. The new formalism has the ability to capture semantic relationships between any two labels on the same object. Results obtained from the model showed an improvement in object classification as a result of exploiting label relationships. However, the major limitation of the approach is that it is too general in nature and is only limited to domains with hierarchical and exclusion relationships. A study [20] developed a deep learning model for multiple-instance learning (MIL) frameworks, whose goal was to perform vision tasks such as classification and image annotation. In the model, each image object uses two instance sets of object proposals and text annotations to perform vision tasks.