Large-scale Text-based Video Classification using Contextual Features

Ibrahim, Zein Al Abidin; Haidar, Siba; Sbeity, Ihab

doi:10.24018/ejece.2019.3.2.68

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it may be difficult to learn what the features of these videos are and to distinguish the content of the captured video. Artificial intelligence helps us analyze videos, extract their features, and ultimately classify them [22]. This classification process is done with ML algorithms.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Video Classification Results with Machine Learning

Akarsu,

Karacali

2024

View full text Add to dashboard Cite

The article is about the classification of anomaly detection dataset-UCF videos obtained from Kaggle. The data set consists of 4-class video files. While three of them consist of different crimes and abnormal events such as Burglary, Explosion, and Fighting, one class consists of videos containing quotes from daily life that do not contain different crimes or events. 4-class videos from Anomaly-Detection-Dataset-UCF were used, Burglary consists of 100 videos, and videos from the other 3 classes are 50 each. and the number of frames of each video varies. First, each video in the dataset was converted into image frames. Because video data is the combination of multiple image frames to form a series. and each picture frame in this series contains important information. To obtain this information, feature information of each frame was extracted. This feature extraction process was carried out with 3 different algorithms. These algorithms; Alexnet is Vgg19 and Resnet18. However, during the feature acquisition phase, the last three layers from each of the 3 pre-training algorithms were removed with Transfer Learning, and 4 layers were manually added instead. and retrained Alexnet, vgg19, and resnet18 pretraining algorithms. Thus, it was aimed to obtain 1500 features instead of 1000 from each pre-training algorithm. It was aimed to obtain more detailed information about the videos by extracting more features. that is, it was aimed to increase the number of features with transfer learning. In other words, in this study, transfer learning was used for feature improvement and the effects of these three different algorithms on classification accuracy with the same parameters were investigated. The classification was done in MATLAB with an LSTM classifier as 5-fold. The data is divided into 5 parts with 5 folds, and different data are divided into test and train in each cycle. In this way, all data is reviewed and accuracy is verified at each fold. In the end, the classification result is obtained by averaging the accuracy in 5 cycles. Among the 3 algorithms, with both transfer learning and 5-fold classification, the lowest result was obtained from the Alexnet pre-training algorithm with 94.3% accuracy, Vgg19 with 96% accuracy, and the highest result was obtained from resnet18 with 98% accuracy. The content of the study focuses on examining these truths in detail.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of Video Classification Results with Machine Learning

Akarsu,

Karacali

2024

View full text Add to dashboard Cite

show abstract

“…By analyzing the work performed in this area, it becomes evident that the text-based approach is less explored, which can be attributed to the lack of textual information regarding movies Ibrahim, Haidar and Sbeity (2019). In addition, most textual information (such as transcripts, text, and oral captions) is mostly conversational.…”

Section: A Review Of Research Literaturementioning

confidence: 99%

A Movie recommender system based on topic modeling using machine learning methods

Kordabadi

Nazari

Mansoorizadeh

2022

Preprint

View full text Add to dashboard Cite

In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user’s age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user’s age and recommend movies based on the user’s age with 93% accuracy, which is highly satisfactory.

show abstract

“…YouTube is known as the largest repository of videos and is widely used for video sharing by billions of users [1]- [4]. However, given the massive number of videos on the web, users face difficulties in accurately retrieving and obtaining the videos they need [5], [6]. The best method to examine, extract and classify web videos on the basis of content similarity is web video categorization (WVC) [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing multi-class web video categorization model using machine and deep learning approaches

Yafooz

Alsaeedi

Alluhaibi

et al. 2022

IJECE

View full text Add to dashboard Cite

<p><span>With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.</span></p>

show abstract

Large-scale Text-based Video Classification using Contextual Features

Cited by 6 publications

References 7 publications

Comparison of Video Classification Results with Machine Learning

Comparison of Video Classification Results with Machine Learning

A Movie recommender system based on topic modeling using machine learning methods

Enhancing multi-class web video categorization model using machine and deep learning approaches

Contact Info

Product

Resources

About