Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis

Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila

doi:10.1109/tnnls.2017.2735364

Cited by 16 publications

(16 citation statements)

References 33 publications

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“…In an unsupervised fashion, they succeeded in detecting and localizing actions, which were considered latent topics. New learning algorithms based on EM and variational Bayes inference were proposed in [29] for activity analysis in videos where the description of activities and behaviors was made by the dynamic topic model. The activities and behaviors were described by a dynamic topic model.…”

Section: Topic Modelingmentioning

confidence: 99%

A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance

Rezaei

Yazdi

2021

Wireless Communications and Mobile Computing

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Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low-level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures. On the other side, topic modeling-based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, K -means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.

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Section: Topic Modelingmentioning

confidence: 99%

A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance

Rezaei

Yazdi

2021

Wireless Communications and Mobile Computing

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“…Isupova et al (Isupova et al, 2017) studied approaches for automatic analysis of behaviour. They achieved this using a learning mechanism for a topic model which they proposed.…”

Section: Detection Of Astroturfing On Online Social Networkmentioning

confidence: 99%

Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

2019

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Astroturfing is a phenomenon in which sponsors of fake messages or reviews are masked because their intentions are not genuine. Astroturfing reviews are intentionally made to influence people to take decisions in favour of or against a target service or product or organization. The tourism sector being one of the sectors that is flourishing and witnessing unprecedented growth is affected by the activities of astroturfers. Astroturfing reviews can cause many problems to tourists who make decisions based on available online reviews. However, authentic and genuine reviews help people make informed decisions. In this paper a Latent Dirichlet Allocation (LDA) based Group Topic-Author model is proposed for efficient discovery of social astroturfing groups within the tourism domain. An algorithm named Astroturfing Group Topic Detection (AGTD) is defined for the implementation of the proposed model. The experimental results of this study revealed the utility of the proposed system for the discovery of social astroturfing groups within the tourism domain.

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“…To the best of our knowledge, the dynamic topic model is the first time topic model that captures the evolution of topics in a sequentially organized corpus of documents [7]. Based on the time topic model, a series of applications are researched, for example, dynamic features extraction [15], automated behaviour analysis [16], travel recommendation [17], and tracking urban geotopics [18].…”

Section: Introductionmentioning

confidence: 99%

A Method for Constructing Supervised Time Topic Model Based on Variational Autoencoder

Gou

Zheng

2021

Scientific Programming

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Topic modeling is a probabilistic generation model to find the representative topic of a document and has been successfully applied to various document-related tasks in recent years. Especially in the supervised topic model and time topic model, many methods have achieved some success. The supervised topic model can learn topics from documents annotated with multiple labels and the time topic model can learn topics that evolve over time in a sequentially organized corpus. However, there are some documents with multiple labels and time-stamped in reality, which need to construct a supervised time topic model to achieve document-related tasks. There are few research papers on the supervised time topic model. To solve this problem, we propose a method for constructing a supervised time topic model. By analysing the generative process of the supervised topic model and time topic model, respectively, we introduce the construction process of the supervised time topic model based on variational autoencoder in detail and conduct preliminary experiments. Experimental results demonstrate that the supervised time topic model outperforms several state-of-the-art topic models.

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Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis

Cited by 16 publications

References 33 publications

A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance

A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance

Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

A Method for Constructing Supervised Time Topic Model Based on Variational Autoencoder

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