Congested scene classification via efficient unsupervised feature learning and density estimation-Reference-Cited by-同舟云学术

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Congested scene classification via efficient unsupervised feature learning and density estimation

Published:2016-08 Issue: Volume:56 Page:159-169
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Yuan Yuan,Wan Jia,Wang Qi

Funder

National Basic Research Program of China

State Key Program of National Natural Science of China

National Natural Science Foundation of China

Fundamental Research Funds

Natural Science Foundation Research Project of Shaanxi Province

Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference50 articles.

1. Pairwise constraints based multiview features fusion for scene classification;Yu;Pattern Recognit.,2013

2. Improved scene classification using efficient low-level features and semantic cues;Serrano;Pattern Recognit.,2004

3. Bag of spatio-visual words for context inference in scene classification;Bolovinou;Pattern Recognit.,2013

4. Exemplar based deep discriminative and shareable feature learning for scene image classification;Zuo;Pattern Recognit.,2015

5. Scene classification based on single-layer sae and svm;Yin;Expert Syst. Appl.,2015

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