Sketch and attribute based query interfaces

Tırkaz, Çağlar (2015) Sketch and attribute based query interfaces. [Thesis]

[thumbnail of CaglarTirkaz_10073154.pdf] PDF

Download (6MB)


In this thesis, machine learning algorithms to improve human computer interaction are designed. The two areas of interest are (i) sketched symbol recognition and (ii) object recognition from images. Specifically, auto-completion of sketched symbols and attribute-centric recognition of objects from images are the main focus of this thesis. In the former task, the aim is to be able to recognize partially drawn symbols before they are fully completed. Auto-completion during sketching is desirable since it eliminates the need for the user to draw symbols in their entirety if they can be recognized while they are partially drawn. It can thus be used to increase the sketching throughput; to facilitate sketching by offering possible alternatives to the user; and to reduce user-originated errors by providing continuous feedback. The latter task, allows machine learning algorithms to describe objects with visual attributes such as “square”, “metallic” and “red”. Attributes as intermediate representations can be used to create systems with human interpretable image indexes, zero-shot learning capability where only textual descriptions are available or capability to annotate images with textual descriptions.
Item Type: Thesis
Additional Information: Yükseköğretim Kurulu Tez Merkezi Tez No: 420266.
Uncontrolled Keywords: Sketch recognition. -- Attribute-based learning. -- Machine learning. -- Computer vision. -- Pattern recognition. -- Natural language processing.
Subjects: Q Science > QA Mathematics > QA076 Computer software
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 09 Apr 2018 11:22
Last Modified: 26 Apr 2022 10:15

Actions (login required)

View Item
View Item