基于卷积神经网络与多尺度空间编码的场景识别方法

doi:10.16180/j.cnki.issn1007-7820.2020.12.011

Abstract

Abstract:

A scene image is generally composed of some foreground objects and background contexts with a certain spatial layout. Due to different scales, viewpoints, and backgrounds, there exists large intra-class variation within the same scene class. On the other hand, the common objects also result in a certain inter-class similarities among heterogeneous scenes as well. Consequently, the multi-scale space encoding based on convolutional neural networks (CNN) for scene representation is proposed in the study, which combines multi-scale dense sampling method, CNN algorithm, and multi-scale space encoding method. The multi-scale encoding method spatially partitions the sampling grid many times, and then aggregates the CNN features within sub-regions with different shapes for generating the multi-scale space VLAD. The experiment is carried out on the Scene15 scene dataset, and the test results show that the test accuracy reaches 94.67%.

Key words: scene recognition, convolutional neural networks, K-means clustering, VLAD, PCA, SVM

CLC Number:

TP391

MIAO Ran,LI Feifei,CHEN Qiu. Scene Recognition Algorithm Based on Convolutional Neural Networks and Multi-Scale space Encoding[J].Electronic Science and Technology, 2020, 33(12): 54-58.

Figures/Tables 6

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References 23

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识别算法	识别精度/%
Object Bank^[17]	80.90
SPM^[7]	81.40
MSAE^[18]	81.97
CENTRIST^[19]	83.90
ISPR^[20]	85.10
SDCF^[21]	93.60
ResNet + Weighted Layout^[22]	94.47
DUCA^[23]	94.50
ResNet + Multi-Scale Space VLAD	94.67

Scene Recognition Algorithm Based on Convolutional Neural Networks and Multi-Scale space Encoding

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