Vast volumes of digital video data are generated recently in our daily life. One of the most challenging problems is classifying and retrieving the desired information from huge collections of digital video. Consequently, the closed caption text has been utilized as an alternative to enhance the video retrieval and classification. Some systems are designed based on English closed caption howeve...

Video mining involves the analysis of content-based classification, indexing, and retrieval; representation, browsing, and visualization of the features in the video. This paper mainly is to survey available and potential technologies for video monitoring and mining, the general methods of fast and efficient content-based analysis of video streams and to identify promising

Current state-of-the-art human activity recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame. We propose a simple, yet effective, method for the temporal detection of activities in temporally untrimmed videos with the help of untrimmed classification. Firstly, our model predicts the top k labels for each untrimmed video by analysing...

The segmentation and classification of news video into single-story semantic units is a challenging problem. This research proposes a two-level, multi-modal framework to tackle this problem. The video is analyzed at the shot and story unit (or scene) levels using a variety of features and techniques. At the shot level, we employ a Decision Tree to classify the shot into one of 13 pre-defined ca...

In this work, we present a system for the automated classification of seabed substrates in underwater video. Classification of seabed substrates traditionally requires manual analysis by a marine biologist, according to an established classification system. Accurate, consistent and robust classification is difficult in underwater video due to varying lighting conditions, turbidity and method of...

background: video-based instruction has been stated as an effective method of teaching and its potentials have encouraged instructors learners to opt this method purpose: the current study has compared the students' cognitive learning in the two approaches of lecture- based and video-based teaching. methods: this quasi-experimental study was conducted on 88 senior nursing students who were rand...

In this paper we propose an approach to high-level classification of video into genre: sport, cartoon, news, commercial and music. An important issue for automatic high-level classification systems is the amount of time needed to classify a video. Here we investigate classification performance as a function of the test sequence length. In addition we present performance against different orders...

We present a novel scheme for object-based video sequence representation using appearance spaces. Our scheme enables fully automatic extraction of semantic video objects for a class of sequences, and their supervised organization in an object-class hierarchy. The hierarchy can be used for generic classification of query video objects, and transcoding using semantics of video objects.

This thesis mainly focuses on visual-information based daily activity classification, anomaly detection, and video tracking through using visual sensors. The main reasons for adopting visual-information based methods are due to: (i) vision plays a major role in recognition/classification of activities which is a fundamental issue in a human-centric system; (ii) visual sensor-based analysis may ...

Large-scale datasets have played a significant role in progress of neural network and deep learning areas. YouTube-8M is such a benchmark dataset for general multilabel video classification. It was created from over 7 million YouTube videos (450,000 hours of video) and includes video labels from a vocabulary of 4716 classes (3.4 labels/video on average). It also comes with pre-extracted audio &...