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  4. Batch mode active learning for multimedia pattern recognition
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Batch mode active learning for multimedia pattern recognition

Full metadata

Title
Batch mode active learning for multimedia pattern recognition
Description

The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.

Date Created
2013
Contributors
  • Chakraborty, Shayok (Author)
  • Panchanathan, Sethuraman (Thesis advisor)
  • Balasubramanian, Vineeth N. (Committee member)
  • Li, Baoxin (Committee member)
  • Mittelmann, Hans (Committee member)
  • Ye, Jieping (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • Computer Science
  • artificial intelligence
  • Active learning
  • Multimedia
  • Optimization
  • Active learning
  • Multimedia systems
  • Pattern recognition systems
Resource Type
Text
Genre
Doctoral Dissertation
Academic theses
Extent
xviii, 218 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Reuse Permissions
All Rights Reserved
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.17891
Statement of Responsibility
by Shayok Chakraborty
Description Source
Viewed on Dec. 4, 2013
Level of coding
full
System Created
  • 2013-07-12 06:23:11
System Modified
  • 2021-08-30 01:41:51
  •     
  • 2 years 1 month ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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