An approach and An algorithm for different video lecture classification (by multimedia data)

An approach and An algorithm for different video lecture classification (by multimedia data)

Explain an approach and an algorithm for different video lecture classification (by multimedia data)?

Classification of videos helps to increase efficiency of video retrieval and it is one of the most important tasks. During process of Video classification information is obtained from features extracted out of the video components, videos are then, placed in categories defined earlier. Information including visual and motion features of various components of video like objects, shots and scenes is obtained. Most of the classification techniques are either semantic content classification or non-semantic content classification. The most suitable one is employed as per the type of a video and application and thus, video can be classified to the most suitable and closest among all pre-defined categories. Semantic video classification can be performed at three levels of a video. Video genres, video events and objects in the video, Video genres-based classification is to classify videos into one of the pre-defined categories of videos. These categories of videos are kinds of videos commonly exist like videos of sports, news, cartoons, movies, wildlife, documentary movies, etc. Video genres-based classification has better and broader detection capability while objects and events have narrow detection range. Event based video classification is based on event detection in a video data and to classify it into one of the pre-defined categories. 

An event is said to be occurred if it has significant and visible video content. A video can have many events and each event has sub-events. One of the most important steps in content-based video classification is to classify events of a video. Shots are most elementary component of a video. Classification of shots determines classification of videos. Shots are classified using features of objects in shots. Different kinds of video features, motion, color, texture and edge for every shot are extracted for video retrieval. Image retrieval methods and techniques can be used for key frame-based video retrieval systems. Low level visual features of key-frames are exploited for this purpose. In key-frame based retrieval, as a video is abstracted and represented by features of its key-frames, indexing methods of image database can be applied to shot indexing. Each shot and all its key-frames are linked to each other. For a video retrieval, a shot is searched by identifying its key-frame. Computational cost involved while using all frames of a shot to retrieve a video is much higher than that when only key frames are used to represent a shot. Visual features of these key frames are compared with those of the videos in the database for retrieval Key-frames are also employed in face and object-based video retrieval. A large number of CBVR systems among the existing ones are working with key-frames. Key-frames can deliver a lot of useful information for retrieval purpose and if required, static features of key-frames can also be used to measure video similarity along with motion features and object features.

Object based video classification is based on object detection in video data. Faces and texts are also used as a method to classify videos. Four types of TV programs are classified by method proposed by Dimitrova et al. Faces and Texts are detected and then tracked to each frame of video segment. Frames are labeled for a specific type according to respective faces and texts. An HMM (hidden markov model) is trained to classify each type of frame using their labels. The appearance of textual information while streaming of video frames enables making an automated video retrieval system based on texts appearing in consecutive frames. Video classification using objects such as faces and texts work only in specific environment and this classification for video indexing has the limitation.

How content-based filtering can be used in this case?

Content-Based Filtering

The content-based approach uses additional information about users and/or items. This filtering method uses item features to recommend other items similar to what the user likes and also based on their previous actions or explicit feedback. If we consider the example for a movies recommender system, the additional information can be, the age, the ***, the job or any other personal information for users as well as the category, the main actors, the duration or other characteristics for the movies i.e the items.

The main idea of content-based methods is to try to build a model, based on the available “features”, that explain the observed user-item interactions. Still considering users and movies, we can also create the model in such a way that it could provide us with an insight into why so is happening. Such a model helps us in making new predictions for a user pretty easily, with just a look at the profile of this user and based on its information, to determine relevant movies to suggest.

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