|Table of Contents|

Sparse Gaussian coding for image recognition

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年01期
Page:
61-
Research Field:
Publishing date:

Info

Title:
Sparse Gaussian coding for image recognition
Author(s):
Zhang ShaohuiWang Yiran
College of Network Engineering,Zhoukou Normal University,Zhoukou 466001,China
Keywords:
image recognition deep learning feature representation sparse Gaussian coding feature learning K-means clustering
PACS:
TP391
DOI:
-
Abstract:
In order to solve the malconformation of clustering in the feature learning,the paper presents a sparse Gaussian coding based feature learning algorithm.It can be trained only through K-means clustering.In the encoding process it takes data’s distribution into consideration.Given that the K-means clustering often results in unequal clusters,we also propose a feature selection method that can be used for denoising and dimension reduction.This model achieves high accuracy,and saves training time a lot.In this paper,we have designed a contrast experiment on the face database AR and the object database Caltech101.The experimental results show that the algorithm is effective and robust.

References:

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Last Update: 2016-02-29