A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The limits of expert performance using hierarchic marking menus
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
FlowMenu: combining command, text, and data entry
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Shorthand writing on stylus keyboard
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Command strokes with and without preview: using pen gestures on keyboard for command selection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Using strokes as command shortcuts: cognitive benefits and toolkit support
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A visual approach to sketched symbol recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Protractor: a fast and accurate gesture recognizer
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A lightweight multistroke recognizer for user interface prototypes
Proceedings of Graphics Interface 2010
Understanding users' preferences for surface gestures
Proceedings of Graphics Interface 2010
Gesture search: a tool for fast mobile data access
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Gesture-based interaction: a new dimension for mobile user interfaces
Proceedings of the International Working Conference on Advanced Visual Interfaces
Using embodied allegories to design gesture suites for human-data interaction
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Proceedings of the 25th annual ACM symposium on User interface software and technology
The impact of motion dimensionality and bit cardinality on the design of 3D gesture recognizers
International Journal of Human-Computer Studies
Learning and performance with gesture guides
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The challenges and potential of end-user gesture customization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Your reactions suggest you liked the movie: automatic content rating via reaction sensing
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
CrowdLearner: rapidly creating mobile recognizers using crowdsourcing
Proceedings of the 26th annual ACM symposium on User interface software and technology
The Journal of Machine Learning Research
Understanding the consistency of users' pen and finger stroke gesture articulation
Proceedings of Graphics Interface 2013
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Personal user-defined gesture shortcuts have shown great potential for accessing the ever-growing amount of data and computing power on touchscreen mobile devices. However, their lack of scalability is a major challenge for their wide adoption. In this paper, we present Gesture Marks, a novel approach to touch-gesture interaction that allows a user to access applications and websites using gestures without having to define them first. It offers two distinctive solutions to address the problem of scalability. First, it leverages the "wisdom of the crowd", a continually evolving library of gesture shortcuts that are collected from the user population, to infer the meaning of gestures that a user never defined himself. Second, it combines an extensible template-based gesture recognizer with a specialized handwriting recognizer to even better address handwriting-based gestures, which are a common form of gesture shortcut. These approaches effectively bootstrap a user's personal gesture library, alleviating the need to define most gestures manually. Our work was motivated and validated via a series of user studies, and the findings from these studies add to the body of knowledge on gesture-based interaction.