Next Article in Journal
A 750 K Photocharge Linear Full Well in a 3.2 μm HDR Pixel with Complementary Carrier Collection
Next Article in Special Issue
Measurement of Axial Rigidity and Postural Instability Using Wearable Sensors
Previous Article in Journal
A Circular Microstrip Antenna Sensor for Direction Sensitive Strain Evaluation
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(1), 306;

L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification

Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
Author to whom correspondence should be addressed.
Received: 13 October 2017 / Revised: 5 January 2018 / Accepted: 18 January 2018 / Published: 20 January 2018
(This article belongs to the Special Issue Smart Decision-Making)
Full-Text   |   PDF [5500 KB, uploaded 20 January 2018]   |  


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. View Full-Text
Keywords: decision tree; ensemble tree; image classification; self-organizing map decision tree; ensemble tree; image classification; self-organizing map

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Choi, J.; Song, E.; Lee, S. L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification. Sensors 2018, 18, 306.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top