Abstract
dc:description.abstractInteractive deep learning frameworks are crucial for effectively exploring and analyzing complex image datasets in visual analytics. However, existing approaches often face challenges related to inference accuracy and adaptability. To address these issues, we propose ImageSI, a framework integrating deep learning models with semantic interaction techniques for interactive image data analysis. Unlike traditional methods, ImageSI directly incorporates user feedback into the image model, updating underlying embeddings through customized loss functions, thereby enhancing the performance of dimension reduction tasks. We introduce three variations of ImageSI, ImageSItext{MDS}-1, prioritizing explicit pairwise relationships from user interaction, and ImageSItext{DRTriplet} and ImageSItext{PHTriplet}, emphasizing clustering by defining groups of images based on user input. Through usage scenarios and quantitative analyses centered on algorithms, we demonstrate the superior performance of ImageSItext{DRTriplet} and ImageSItext{MDS}-1 in terms of inference accuracy and interaction efficiency. Moreover, ImageSItext{PHTriplet} shows competitive results. The baseline model, WMDS-1, generally exhibits lower performance metrics.
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Computer Science & Applications
- Department dc:contributor.department
- Computer Science and#38; Applications
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Lin, Jiayue
- Chair dc:contributor.committeechair
-
- North, Christopher L.
- Committee members dc:contributor.committeemember
-
- Faust, Rebecca Jane
- Huang, Lifu
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:40777
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/119283