Very sparse random projections
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ShadowDraw: real-time user guidance for freehand drawing
ACM SIGGRAPH 2011 papers
Edgel index for large-scale sketch-based image search
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Listen, look, and gotcha: instant video search with mobile phones by layered audio-video indexing
Proceedings of the 21st ACM international conference on Multimedia
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Indexing billions of images for sketch-based retrieval
Proceedings of the 21st ACM international conference on Multimedia
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The advent of touch panels in mobile devices has provided a good platform for mobile sketch search. However, most of the previous sketch image retrieval systems usually adopt an inverted index structure on large-scale image database, which is formidable to be operated in the limited memory of mobile devices. In this paper, we propose a novel approach to address these challenges. First, we effectively utilize distance transform (DT) features to bridge the gap between query sketches and natural images. Then these high-dimensional DT features are further projected to more compact binary hash bits. The experimental results show that our method achieves very competitive retrieval performance with MindFinder approach [3] but only requires much less memory storage (e.g., our method only requires 3% of total memory storage of MindFinder in 2.1 million images). Due to its low consumption of memory, the whole system can independently operate on the mobile devices.