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Visipedia - Embedding-driven Visual Feature Extraction and Learning
 Jakeš, Jan ; Beran, Vítězslav (oponent) ; Zemčík, Pavel (vedoucí práce)
Multidimensional embedding is a powerful method of representing similarity measures among objects without the need for their explicit categorization. It has been increasingly used in recent years to annotate objects making an important part of the Visipedia project and its related work. This work explores the possibilities of learning from embedding-annotated images using their visual attributes and develops methods of predicting embedding coordinates for previously unseen images. It studies the relevant feature extraction and learning algorithms and describes the whole process of design and development of such a system using common machine learning approaches. The system is tested and evaluated with two different datasets and the performed experiments present the first results for a task of its kind.
Úplný záznam
Visipedia - Embedding-driven Visual Feature Extraction and Learning
 Jakeš, Jan ; Beran, Vítězslav (oponent) ; Zemčík, Pavel (vedoucí práce)
Multidimensional embedding is a powerful method of representing similarity measures among objects without the need for their explicit categorization. It has been increasingly used in recent years to annotate objects making an important part of the Visipedia project and its related work. This work explores the possibilities of learning from embedding-annotated images using their visual attributes and develops methods of predicting embedding coordinates for previously unseen images. It studies the relevant feature extraction and learning algorithms and describes the whole process of design and development of such a system using common machine learning approaches. The system is tested and evaluated with two different datasets and the performed experiments present the first results for a task of its kind.
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