Automatic Image Region Annotation by Genetic Algorithm-Based Joint Classifier and Feature Selection in Ensemble System
Author(s)
Anh, Vu Luong
Tien, Thanh Nguyen
Xuan, Cuong Pham
Thi, Thu Thuy Nguyen
Liew, Alan Wee-Chung
Stantic, Bela
Year published
2018
Metadata
Show full item recordAbstract
In this paper, we address the image region tagging procedure in which each image region is annotated by a suitable concept. Specifically, we first extract the feature vector for each segmented region. Then we propose a Genetic Algorithm (GA)-based simultaneous classifier and feature selection method working with ensemble system to learn the relationship between the low-level features and high-level concepts. The extensive experiments conducted on two public datasets namely MSRC v1 and MSRC v2 demonstrate the better performance of our method than several well-known ensemble methods, supervised machine learning methods, and ...
View more >In this paper, we address the image region tagging procedure in which each image region is annotated by a suitable concept. Specifically, we first extract the feature vector for each segmented region. Then we propose a Genetic Algorithm (GA)-based simultaneous classifier and feature selection method working with ensemble system to learn the relationship between the low-level features and high-level concepts. The extensive experiments conducted on two public datasets namely MSRC v1 and MSRC v2 demonstrate the better performance of our method than several well-known ensemble methods, supervised machine learning methods, and sparse coding-based methods in the regions-in-image classification task.
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View more >In this paper, we address the image region tagging procedure in which each image region is annotated by a suitable concept. Specifically, we first extract the feature vector for each segmented region. Then we propose a Genetic Algorithm (GA)-based simultaneous classifier and feature selection method working with ensemble system to learn the relationship between the low-level features and high-level concepts. The extensive experiments conducted on two public datasets namely MSRC v1 and MSRC v2 demonstrate the better performance of our method than several well-known ensemble methods, supervised machine learning methods, and sparse coding-based methods in the regions-in-image classification task.
View less >
Journal Title
Lecture Notes in Computer Science
Volume
10751
Subject
Other information and computing sciences not elsewhere classified