Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Chris Tseng

Third Advisor

Rajesh Pradhan

Keywords

Sketches, drawing, datasets, machine learning algorithms, neural networks.

Abstract

Sketching has been used by humans to visualize and narrate the aesthetics of the world for a long time. With the onset of touch devices and augmented technologies, it has attracted more and more attention in recent years. Recognition of free-hand sketches is an extremely cumbersome and challenging task due to its abstract qualities and lack of visual cues. Most of the previous work has been done to identify objects in real pictorial images using neural networks instead of a more abstract depiction of the same objects in sketch. This research aims at comparing the performance of different machine learning algorithms and their learned inner representations. This research studies some of the famous machine learning models in classifying sketch images. It also does a study of legacy and the new datasets to classify a new sketch through various classifiers like support vector machines and the use of deep neural networks. It achieved remarkable results but still lacking behind the accuracy in the classification of the sketch images.

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