DocumentCode :
730214
Title :
Horizontal flip-invariant sketch recognition via local patch hashing
Author :
Bozas, Konstantinos ; Izquierdo, Ebroul
Author_Institution :
Sch. of EECS, Queen Mary Univ. of London, London, UK
fYear :
2015
fDate :
19-24 April 2015
Firstpage :
1146
Lastpage :
1150
Abstract :
This paper introduces a flip aware patch matching frame-work that facilitates scalable sketch recognition. An overlapping spatial grid is utilized to generate an ensemble of patches for each sketch. We rank similarities between freely drawn sketches via a spatial voting process where similar patches in terms of shape and structure arbitrate for the result. Patch similarity is efficiently estimated via the min-hash algorithm. A novel spatial aware reverse index structure ensures the scalability of our scheme. We show the benefits of horizontal flip invariance and structural information in sketch recognition and demonstrate state-of-the-art results in two challenging sketch datasets.
Keywords :
cryptography; image matching; flip aware patch matching frame-work; freely drawn sketches; horizontal flip invariance; horizontal flip-invariant sketch recognition; local patch hashing; min-hash algorithm; overlapping spatial grid; patch similarity; scalable sketch recognition; sketch datasets; spatial aware reverse index structure; spatial voting process; structural information; Artificial neural networks; Neuroscience; Presses; Process control; Shape; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
Type :
conf
DOI :
10.1109/ICASSP.2015.7178149
Filename :
7178149
Link To Document :
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