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Ravi, T. N.
- Video Taxonomy Identify Using Frame-Based Naive Similarity Finder Algorithm
Authors
1 Department of Computer Science, PSGR Krishnammal College for Women, IN
2 E.V.R. College, Trichy, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 12 (2013), Pagination: 496-501Abstract
Due to the advances in multimedia applications, large databases of videos require efficient methods that enable fast browsing and accessing the information pursued. Most of video data are stored in personal video recorders (PVRs) such as DVD recorders and hard disc recorders. We propose a video summarization approach for PVRs application, which is based on two-level repetitive information detection and content analysis. First the original video sequence is divided into shots and scenes, and key frames are extracted from these shots. Then it removes redundant video content in the shot level. Impact factors of scenes and key frames are defined, and parts of shots are selected to generate the initial video summary. Finally a repetitive frame segment detection step is used to remove redundant information in the initial video summary. With the two-level redundancy analysis procedure, this tool could remove almost all repetitive information.
Keywords
Key Frames, Dense Segment Extraction, Video Indexing.- DCFMRS: Deep Collaborative Filtering for Movie Recommender System
Authors
1 Department of Computer Science, Periyar E.V.R. College (Autonomous) (Affiliated to Bharathidasan University), Tiruchirappalli, Tamil Nadu, IN
Source
Digital Signal Processing, Vol 11, No 12 (2019), Pagination: 212-215Abstract
Entertainment industry in this internet era has constantly been taking huge interest in ensuring a tailored experience to each of its audience. Recommender systems are a subclass of information filtering systems and suggesting items especially in streaming services. Streaming services like movie recommendation systems are essential for finding similar users and items. This paper presents deep learning approach based on collaborative filtering that can handle cold start and overfitting problems to provide more reliable predictions. User and item-based collaborative filtering are combined to identify highly items. These items are used to train the deep learning model to predict user ratings on new items and to provide final recommendations. The experimental result of the proposed model has been compared with that of the state of art models in terms of MAE and RMSE.