基于稀疏自动编码机的场景识别算法

doi:10.16180/j.cnki.issn1007-7820.2019.01.008

Abstract

Abstract:

To narrow the gap between low-level features and high-level concepts in scene recognition, a new algorithm based on the sparse autoencoder was proposed. This algorithm adopted the feature encoding technique that combined the sparse autoencoder and spatial pyramid pooling. First of all, the local HOG descriptors were extracted from scene images, then they were encoded into sparse features by the modified sparse autoencoder. After spatial pyramid pooling and local normalization on these sparse features, the image representation can be obtained. Finally, linear SVM was utilized to implement scene recognition. The experimental results on Scene-15 dataset indicated that the recognition accuracy of this algorithm can be increased up to 81.97%.

Key words: scene recognition, sparse autoencoder, spatial pyramid pooling, local normalization, HOG, SVM

CLC Number:

TP391

XIE Lin,LI Feifei,CHEN Qiu. Scene Recognition Algorithm Based on Sparse Autoencoder[J].Electronic Science and Technology, 2019, 32(1): 38-41.

Figures/Tables 6

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References 16

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方法	隐含层中节点的个数	识别率/%
传统的稀疏自动编码机(SAE)	1024	78.58
传统的稀疏自动编码机(SAE)	2048	79.36
改进的稀疏自动编码机(MSAE)	1024	80.85
改进的稀疏自动编码机(MSAE)	2048	81.97

方法	识别率/%
MSAE	81.97
RBoW ^[13]	78.60
ScSPM ^[14]	80.28
Object Bank ^[15]	80.90
SPM ^[11]	81.40
DSPM ^[16]	81.81

Scene Recognition Algorithm Based on Sparse Autoencoder

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