Name
Abstract | ||
|---|---|---|
Constructing a distinctive and robust sketch descriptor is one of the most challenging problems in sketch based applications. In this paper, a new hand-drawn sketch descriptor is proposed. The proposed descriptor utilizes the statistic information of multiple features and bag-of-features representation to achieve translation and scale invariance and rotation robustness. The proposed descriptor also encodes the information entropy distribution of information point located on the contour, which can describe the intrinsic property of sketch better and is more robust to noises. Experimental results demonstrate the validity and efficiency of the proposed sketch descriptor. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2016.03.098 | Neurocomputing |
Keywords | Field | DocType |
Sketch descriptor,Bag-of-features,Information entropy,Clustering | Intrinsic and extrinsic properties (philosophy),Scale invariance,GLOH,Statistic,Pattern recognition,Computer science,Robustness (computer science),Artificial intelligence,Cluster analysis,Entropy (information theory),Machine learning,Sketch | Journal |
Volume | Issue | ISSN |
213 | C | 0925-2312 |
Citations | PageRank | References |
2 | 0.37 | 10 |
Authors | ||
5 | ||