Currently, image databases have been growing, resulting in the need for optimization and acceleration of the image retrieval and classification processes, together with the improvement of the quality of the returned results. In this context, this work proposes the use of active learning strategies for image classification and retrieval, in order to select more informative samples and to minimize the interaction of the specialist during the learning process. In addition, new active learning strategies are proposed for classification and content-based image retrieval tasks. To validate the proposals, experiments were performed using datasets from different application domains. From the obtained results, it is possible to observe significant g...
Active learning is a label-efficient machine learning method that actively selects the most valuable...
multimodal fusion Abstract: Motivated by the widespread adoption of social networks and the abundant...
Abstract For learning problems where human supervision is expens-ive, active query selection methods...
Abstract—Active learning methods have been considered with increased interest in the statistical lea...
This paper deals with content-based image indexing and category retrieval in general databases. Stat...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper discusses methods for...
Técnicas de aprendizagem vêm sendo empregadas em diversas áreas de aplicação (medicina, biologia, se...
The topic of this thesis is the combination of active learning strategies used in conjunction with ...
Active learning methods have been consid-ered with an increasing interest for user inter-active syst...
With active learning, domain expert doesn't need to annotate the whole dataset, but only those which...
In the context of image search and classification, we describe an active learning strategy that reli...
Within the Artificial Intelligence field, and, more specifically, in the context of Machine Learnin...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
In the context of image search and classification, we describe an active learning strategy that reli...
University of Minnesota Ph.D. dissertation. June 2011. Major: Computer Science. Advisor: Nikolaos P....
Active learning is a label-efficient machine learning method that actively selects the most valuable...
multimodal fusion Abstract: Motivated by the widespread adoption of social networks and the abundant...
Abstract For learning problems where human supervision is expens-ive, active query selection methods...
Abstract—Active learning methods have been considered with increased interest in the statistical lea...
This paper deals with content-based image indexing and category retrieval in general databases. Stat...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper discusses methods for...
Técnicas de aprendizagem vêm sendo empregadas em diversas áreas de aplicação (medicina, biologia, se...
The topic of this thesis is the combination of active learning strategies used in conjunction with ...
Active learning methods have been consid-ered with an increasing interest for user inter-active syst...
With active learning, domain expert doesn't need to annotate the whole dataset, but only those which...
In the context of image search and classification, we describe an active learning strategy that reli...
Within the Artificial Intelligence field, and, more specifically, in the context of Machine Learnin...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
In the context of image search and classification, we describe an active learning strategy that reli...
University of Minnesota Ph.D. dissertation. June 2011. Major: Computer Science. Advisor: Nikolaos P....
Active learning is a label-efficient machine learning method that actively selects the most valuable...
multimodal fusion Abstract: Motivated by the widespread adoption of social networks and the abundant...
Abstract For learning problems where human supervision is expens-ive, active query selection methods...