Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos

2014 ◽  
Vol 23 (12) ◽  
pp. 5198-5208 ◽  
Author(s):  
Jinqiao Wang ◽  
Wei Fu ◽  
Hanqing Lu ◽  
Songde Ma
Keyword(s):  
Topic Model ◽  
Scene Analysis ◽  
Surveillance Videos ◽  
Sparse Topic Model

Anomaly detection in traffic surveillance with sparse topic model

2018 ◽  
Vol 25 (9) ◽  
pp. 2245-2257 ◽  
Author(s):  
Li-min Xia ◽  
Xiang-jie Hu ◽  
Jun Wang
Keyword(s):  
Anomaly Detection ◽  
Topic Model ◽  
Traffic Surveillance ◽  
Sparse Topic Model

scholarly journals SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 451-457
Author(s):  
Qiqi Zhu ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Semantic Feature ◽  
Scene Classification ◽  
Probabilistic Topic Model ◽  
Sparse Topic Model

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

scholarly journals A Sparse Topic Model for Bursty Topic Discovery in Social Networks

2020 ◽  
Vol 17 (5) ◽  
pp. 816-824
Author(s):  
Lei Shi ◽  
Junping Du ◽  
Feifei Kou
Keyword(s):  
Topic Model ◽  
State Of The Art ◽  
Sina Weibo ◽  
Short Text ◽  
Qualitative And Quantitative ◽  
Topic Discovery ◽  
Spike And Slab Prior ◽  
Automatic Discovery ◽  
The Common ◽  
Sparse Topic Model

Bursty topic discovery aims to automatically identify bursty events and continuously keep track of known events. The existing methods focus on the topic model. However, the sparsity of short text brings the challenge to the traditional topic models because the words are too few to learn from the original corpus. To tackle this problem, we propose a Sparse Topic Model (STM) for bursty topic discovery. First, we distinguish the modeling between the bursty topic and the common topic to detect the change of the words in time and discover the bursty words. Second, we introduce “Spike and Slab” prior to decouple the sparsity and smoothness of a distribution. The bursty words are leveraged to achieve automatic discovery of the bursty topics. Finally, to evaluate the effectiveness of our proposed algorithm, we collect Sina weibo dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed STM algorithm outperforms favorably against several state-of-the-art methods

Bayesian Sparse Topic Model

2013 ◽  
Vol 74 (3) ◽  
pp. 375-389 ◽  
Author(s):  
Jen-Tzung Chien ◽  
Ying-Lan Chang
Keyword(s):  
Topic Model ◽  
Sparse Topic Model

scholarly journals SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 451-457
Author(s):  
Qiqi Zhu ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Semantic Feature ◽  
Scene Classification ◽  
Probabilistic Topic Model ◽  
Sparse Topic Model

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

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