News Video Indexing and Retrieval System Using Feature-Based Indexing and Inserted-Caption Detection Retrieval
News Video Indexing and Retrieval System Using Feature-Based Indexing and InsertedCaption Detection Retrieval
Akshay Kumar Singh, Soham Banerjee, Sonu Kumar and Asst. Prof. Mr. S. Ghatak Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, India.
Abstract—Data compression coupled with the availability of high bandwidth networks and storage capacity have created the overwhelming production of multimedia content, this paper briefly describes techniques for content-based analysis, retrieval and filtering of News Videos and focuses on basic ...view middle of the document...
Compared with other video features, information in caption text is highly compact and structured, thus is more suitable for video indexing. However, extracting captions embedded in video frames is a difficult task. In comparison to OCR for document images, caption extraction and recognition in videos involves several new challenges. First, captions in videos are often embedded in complex backgrounds, making caption detection much more difficult. Second, characters in captions tend to have a very low resolution since they are usually made small to avoid obstructing scene objects in a video frame . Indexing can be classified into 2 types I: feature based. II: Annotation based.
Here we are going to give you a brief explanation about feature based indexing which can be further classified into: 1. Segment Based. 2. Object Based. 3. Index Based. Finding the required video and its Retrieval can be successfully carried out by identifying and using the best video query scheme among the group of video queries available. Some of the queries scheme for video retrieval that is worth mentioning are: 1. Query content 2. Query using matching 3. Query function 4. Query behavior 5. Query temporal unit, etc. In query content we try to specify the content of video in the query, in order to retrieve the most suitable match video. It is further classified on the type of contents that is used in the query for video retrieval. Semantic (information) query is the most complex type of query in video database, and it depends on the technologies such as computer vision, machine learning, and AI (Artificial Intelligence). For example: finding scenes with Actor = ―nasseerudin shah‖ and Emotion =‖fighting‖. Audiovisual (AV) query depends on the AV features of the video For example: finding shots where camera is stationary and lens action is zoom-in. Meta query attempts to extract the information about the video data For example: finding out a video directed by Stephen Spielberg and titled ―Jurassic Park‖.IN query using matching we extract matching objects from the database. AV features such as sound and image analysis is used to match the query sample and the video data. Exact-match query Requires exact match between the query and the video whereas Similarity-match query (Often known as Query by Example) Because of the complex nature of video data, this query type is more required Query functions or query behavior depends on the functions that queries perform like Location-deterministic query, Browsing query, Iterative query, Tracking queries, Statistical queries. Query temporal unit classify the granularity of video data required to satisfy query Unit (or video-stream based) query deals with the complete units of video. For example: finding a sport video, which has player A in it, Sub-unit query deals with parts of video data, such as frames, clips and scenes. For example: finding the scenes where actors X appears, finding the shots in which a player with ‗this type of...