L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification
The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/1/306 |
| _version_ | 1798042412639059968 |
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| author | Jaesung Choi Eungyeol Song Sangyoun Lee |
| author_facet | Jaesung Choi Eungyeol Song Sangyoun Lee |
| author_sort | Jaesung Choi |
| collection | DOAJ |
| description | The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion. Consequently, they suffer from several drawbacks in terms of performance and environmental sensitivity. To overcome these limitations, this paper introduces a new tree induction algorithm that classifies images on the basis of local area learning. To train our predictive model, we extract a random local area within the image and use it as a feature for classification. In addition, the self-organizing map, which is a clustering technique, is used for node learning. We also adopt a random sampled optimization technique to search for the optimal node. Finally, each trained node stores the weights that represent the training data and class probabilities. Thus, a recursively trained tree classifies the data hierarchically based on the local similarity at each node. The proposed tree is a type of predictive model that offers benefits in terms of image’s semantic energy conservation compared with conventional tree methods. Consequently, it exhibits improved performance under various conditions, such as noise and illumination changes. Moreover, the proposed algorithm can improve the generalization ability owing to its randomness. In addition, it can be easily applied to ensemble techniques. To evaluate the performance of the proposed algorithm, we perform quantitative and qualitative comparisons with various tree-based methods using four image datasets. The results show that our algorithm not only involves a lower classification error than the conventional methods but also exhibits stable performance even under unfavorable conditions such as noise and illumination changes. |
| first_indexed | 2024-04-11T22:35:12Z |
| format | Article |
| id | doaj.art-a375480048fa4ab9af2839108439a2e9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:35:12Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a375480048fa4ab9af2839108439a2e92022-12-22T03:59:14ZengMDPI AGSensors1424-82202018-01-0118130610.3390/s18010306s18010306L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image ClassificationJaesung Choi0Eungyeol Song1Sangyoun Lee2Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, KoreaDepartment of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, KoreaDepartment of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, KoreaThe decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion. Consequently, they suffer from several drawbacks in terms of performance and environmental sensitivity. To overcome these limitations, this paper introduces a new tree induction algorithm that classifies images on the basis of local area learning. To train our predictive model, we extract a random local area within the image and use it as a feature for classification. In addition, the self-organizing map, which is a clustering technique, is used for node learning. We also adopt a random sampled optimization technique to search for the optimal node. Finally, each trained node stores the weights that represent the training data and class probabilities. Thus, a recursively trained tree classifies the data hierarchically based on the local similarity at each node. The proposed tree is a type of predictive model that offers benefits in terms of image’s semantic energy conservation compared with conventional tree methods. Consequently, it exhibits improved performance under various conditions, such as noise and illumination changes. Moreover, the proposed algorithm can improve the generalization ability owing to its randomness. In addition, it can be easily applied to ensemble techniques. To evaluate the performance of the proposed algorithm, we perform quantitative and qualitative comparisons with various tree-based methods using four image datasets. The results show that our algorithm not only involves a lower classification error than the conventional methods but also exhibits stable performance even under unfavorable conditions such as noise and illumination changes.http://www.mdpi.com/1424-8220/18/1/306decision treeensemble treeimage classificationself-organizing map |
| spellingShingle | Jaesung Choi Eungyeol Song Sangyoun Lee L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification Sensors decision tree ensemble tree image classification self-organizing map |
| title | L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification |
| title_full | L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification |
| title_fullStr | L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification |
| title_full_unstemmed | L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification |
| title_short | L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification |
| title_sort | l tree a local area learning based tree induction algorithm for image classification |
| topic | decision tree ensemble tree image classification self-organizing map |
| url | http://www.mdpi.com/1424-8220/18/1/306 |
| work_keys_str_mv | AT jaesungchoi ltreealocalarealearningbasedtreeinductionalgorithmforimageclassification AT eungyeolsong ltreealocalarealearningbasedtreeinductionalgorithmforimageclassification AT sangyounlee ltreealocalarealearningbasedtreeinductionalgorithmforimageclassification |