Automatic Video Classification Research Articles

On the Use of Deep Learning for Video Classification

The video classification task has gained significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition of the importance of the video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are several existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers do not include the recent state-of-art works, and they also have some limitations. To provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the datasets used. To make the review self-contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided. The critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.

Automatic Video Classification: A Review

INTRODUCTION: In last few years number of internet users and available bandwidth has been increased exponentially. The availability of internet with such a low cost is making audiovisual content a more popular and easier form of information exchange. The internet is having a huge amount of this au

Exploiting Mid-Level Semantics for Large-Scale Complex Video Classification

As the amount of available video data has grown substantially, automatic video classification has become an urgent yet challenging task. Most video classification methods focus on acquiring discriminative spacial visual features and motion patterns for video representation, especially deep learning methods, which have achieved very good results on action recognition problems. However, the performance of most of these methods drastically degenerates for more generic video classification tasks where the video contents are much more complex. Thus, in this paper, the mid-level semantics of videos are exploited to bridge the semantic gap between low-level features and high-level video semantics. Inspired by the term ``frequency-inverse document frequency'', a word weighting method for the problem of text classification is introduced to the video domain. The visual objects in videos are regarded as the words in texts, and two new weighting methods are proposed to encode videos by weighting visual objects according to the characteristics of videos. In addition, the semantic similarities between video categories and visual objects are introduced from the text domain as privileged information to facilitate classifier training on the obtained semantic representations of videos. The proposed semantic encoding method (semantic stream) is then fused with the popular two-stream CNN model for the final classification results. Experiments are conducted on two large-scale complex video datasets, CCV and ActivityNet. The experimental results validate the effectiveness of the proposed methods.

Web video classification with visual and contextual semantics

SummaryOn the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.

Implementation of Automatic X-rated Video Classification and Management System based on Multimodal Features

Implementation of Automatic X-rated Video Classification and Management System based on Multimodal Features

A Probabilistic Approach for Vision-Based Fire Detection in Videos

Automated fire detection is an active research topic in computer vision. In this paper, we propose and analyze a new method for identifying fire in videos. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled background. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content. In the latter case, there are large variations in fire and background characteristics depending on the video instance. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these features are powerful discriminants. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. In addition, a priori knowledge of fire events captured in videos is used to significantly improve the classification results. For edited newscast videos, the fire region is usually located in the center of the frames. This fact is used to model the probability of occurrence of fire as a function of the position. Experiments illustrated the applicability of the method.

Automatic Video Classification: A Survey of the Literature

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> There is much video available today. To help viewers find video of interest, work has begun on methods of automatic video classification. In this paper, we survey the video classification literature. We find that features are drawn from three modalities—text, audio, and visual—and that a large variety of combinations of features and classification have been explored. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research. </para>

An Automatic Video Classification System Based on a Combination of HMM and Video Summarization

As digital libraries and video databases grow, we need methods to assist us in the synthesis and analysis of digital video. Since the information in video databases can be measured in thousands of gigabytes of uncompressed data, tools for efficient summarizing and indexing of video sequences are indispensable. In this paper, we present a method for effective classification of different types of videos that makes use of a video summarization that is in the form of a storyboard of keyframes. To produce the summarization, we first generate a universal basis on which to project a video frame that effectively reduces any video to the same lighting conditions. Each frame is represented by a compressed chromaticity signature. We then set out a multi-stage hierarchical clustering method to efficiently summarize a video. Finally we classify TV videos using a trained Hidden Markov Model that utilizes the compressed chromaticity signatures as well as temporal features of videos derived from their keyframe summaries.