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Journal of System Simulation

Abstract

Abstract: With the exponential growth of data and the complexity of algorithm, efficient computing of DBN (Deep Belief Network,) has become an important issue. A fast training model for image classification of DBN was built according to sample images of DBN have nothing to do with spatial information, an improved algorithm simply as LSMI(Linear Superposition Multiple Images) was proposed for classifying images based on the idea that linear combination of multiple images. The characteristics of training images have nothing to do with spatial information was proved via information entropy theory, meanwhile, the characteristics was identified to be correct based on ORL database. According to ergodic theory, an algorithm simply as LSMI was proposed according to the ergodic theory. The LSMI algorithm was contrasted to other improved algorithms, using COREL and MIT database, judging whether the LSMI algorithm is effective from the correct recognition rate, time complexity and other quotas. The simulation results show that LSMI algorithm can ensure recognition rate, decrease training time greatly and achieve the goal of fast learning.

First Page

549

Revised Date

2014-10-13

Last Page

558

CLC

TP18

Recommended Citation

Gao Qiang, Yang Wu, Li Qian. Fast Training Model for Image Classification Based on Spatial Information of Deep Belief Network[J]. Journal of System Simulation, 2015, 27(3): 549-558.

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