Local extreme learning machine: local classification model for shape feature extraction

  title={Local extreme learning machine: local classification model for shape feature extraction},
  author={Jing Zhang and Lin Feng and Bin Wu},
  journal={Neural Computing and Applications},
The shape feature of an object represents the geometrical information which plays an important role in the image understanding and image retrieval. How to get an excellent shape feature that has rotation, scaling and translation (RST) invariance is a problem in this field. This paper proposed a novel local extreme learning machine (LELM) classification algorithm to extract the shape features. LELM finds nearest neighbors of the testing set from the original training set and trains a local… CONTINUE READING


Publications citing this paper.
Showing 1-2 of 2 extracted citations


Publications referenced by this paper.
Showing 1-10 of 31 references

Support Vector Shape: A Classifier-Based Shape Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2013
View 1 Excerpt

Extreme Learning Machine for Regression and Multiclass Classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) • 2012
View 1 Excerpt

Convex shape decomposition

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition • 2010
View 1 Excerpt

Digital image processing, 3rd edn. Electronic Industry Press, Beijing

C Gonzalez Rafael, E Woods Richard
View 2 Excerpts

Shape matching and object recognition using shape contexts

2010 3rd International Conference on Computer Science and Information Technology • 2010
View 1 Excerpt

Similar Papers

Loading similar papers…