Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
The Trace Transform and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Recognition of Sketched Electrical Diagrams
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Sketched Symbol Recognition using Zernike Moments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Incremental On-line Parsing Algorithm for Recognizing Sketching Diagrams
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Envisioning sketch recognition: a local feature based approach to recognizing informal sketches
Envisioning sketch recognition: a local feature based approach to recognizing informal sketches
A combinatorial approach to multi-domain sketch recognition
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
Recognizing interspersed sketches quickly
Proceedings of Graphics Interface 2009
Iconic and multi-stroke gesture recognition
Pattern Recognition
Combining geometry and domain knowledge to interpret hand-drawn diagrams
Computers and Graphics
A visual approach to sketched symbol recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Constellation models for sketch recognition
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Sketched symbol recognition with auto-completion
Pattern Recognition
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
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Image-based approaches to sketch recognition typically cast sketch recognition as a machine learning problem. In systems that adopt image-based recognition, the collected ink is generally fed through a standard three stage pipeline consisting of the feature extraction, learning and classification steps. Although these approaches make regular use of machine learning, existing work falls short of presenting a proper treatment of important issues such as feature extraction, feature selection, feature combination, and classifier fusion. In this paper, we show that all these issues are significantfactors, which substantially affect the ultimate performance of a sketch recognition engine. We support our case by experimental results obtained from two databases using representative sets of feature extraction, feature selection, classification, and classifier combination methods. We present the pros and cons of various choices that can be made while building sketch recognizers and discuss their trade-offs.