This paper investigates the development of accurate and efficient classifiers to identify misbehaving users (i.e., “flashers”) in a mobile video chat application. Our analysis is based on video session data collected from a mobile client that we built that connects to a popular random video chat service. We show that prior imagebased classifiers designed for identifying normal and misbehaving u...

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video...

Video classification is an essential step towards video perceptive. In recent years, the concept of utilizing association rules for classification emerged. This approach is more efficient and accurate than traditional techniques. Associative classifier integrates two data mining tasks such as association rule discovery and classification, to build a classifier for the purpose of prediction. The...

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of ...

Audio-visual documents obtained from German TV news are classified according to the IPTC topic categorization scheme. To this end usual text classification techniques are adapted to speech, video, and non-speech audio. For each of the three modalities word analogues are generated: sequences of syllables for speech, “video words” based on low level color features (color moments, color correlogra...

In this paper, a four-dimensional spatiotemporal shape context descriptor is introduced and used for human activity recognition in video. The spatiotemporal shape context is computed on silhouette points by binning the magnitude and direction of motion at every point with respect to given vertex, in addition to the binning of radial displacement and angular offset associated with the standard 2...

With the development of the multimedia technology, there are more and more video resources on the Internet, which are difficult to automatically recognize, classify and index. So solve these problems, we present a novel video content understanding scheme in this paper. This scheme is based on the combination strategy of different video features. To represent these video features, we use nine st...

This paper is on action localization in video with the aid of spatio-temporal proposals. To alleviate the computational expensive segmentation step of existing proposals, we propose bypassing the segmentations completely by generating proposals directly from the dense trajectories used to represent videos during classification. Our Action localization Proposals from dense Trajectories (APT) use...

Most of traditional video action recognition methods are based on trimmed videos, which is only one action in one video. But most of videos in real world is untrimmed. In order to overcome the difficulty in some extent, we propose a method based on fusion of multiple features for untrimmed video classification task of ActivityNet challenge 2017. We use the CNN features, MBH features and stacked...