Connected Components Labeling represents a fundamental step for many Computer Vision and Image Processing pipelines. Since the first appearance of the task in the sixties, many algorithmic solutions to optimize the computational load needed to label an image have been proposed. Among them, block-based scan approaches and decision trees revealed to be some of the most valuable strategies. However, due to the cost of the manual construction of optimal decision trees and the computational limitations of automatic strategies employed in the past, the application of blocks and decision trees has been restricted to small masks, and thus to 2D algorithms. With this paper we present a novel heuristic algorithm based on decision tree learning methodology, called Entropy Partitioning Decision Tree (EPDT). It allows to compute near-optimal decision trees for large scan masks. Experimental results demonstrate that algorithms based on the generated decision trees outperform state-of-the-art competitors.

A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes / Söchting, Maximilian; Allegretti, Stefano; Bolelli, Federico; Grana, Costantino. - (2021), pp. 7751-7758. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a Milan, Italy nel Jan 10-15) [10.1109/ICPR48806.2021.9413096].

A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes

Allegretti, Stefano;Bolelli, Federico;Grana, Costantino
2021

Abstract

Connected Components Labeling represents a fundamental step for many Computer Vision and Image Processing pipelines. Since the first appearance of the task in the sixties, many algorithmic solutions to optimize the computational load needed to label an image have been proposed. Among them, block-based scan approaches and decision trees revealed to be some of the most valuable strategies. However, due to the cost of the manual construction of optimal decision trees and the computational limitations of automatic strategies employed in the past, the application of blocks and decision trees has been restricted to small masks, and thus to 2D algorithms. With this paper we present a novel heuristic algorithm based on decision tree learning methodology, called Entropy Partitioning Decision Tree (EPDT). It allows to compute near-optimal decision trees for large scan masks. Experimental results demonstrate that algorithms based on the generated decision trees outperform state-of-the-art competitors.
2021
25th International Conference on Pattern Recognition, ICPR 2020
Milan, Italy
Jan 10-15
7751
7758
Söchting, Maximilian; Allegretti, Stefano; Bolelli, Federico; Grana, Costantino
A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes / Söchting, Maximilian; Allegretti, Stefano; Bolelli, Federico; Grana, Costantino. - (2021), pp. 7751-7758. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a Milan, Italy nel Jan 10-15) [10.1109/ICPR48806.2021.9413096].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1212419
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