Summarization and Visualization of Large Volumes of Broadcast Video Data
Kumar Abhishek, Ashok Yogi

TL;DR
This paper presents a robust method for detecting and classifying different band elements in news video frames, enabling effective summarization and storage of large broadcast video datasets.
Contribution
It introduces a novel combination of probabilistic Hough transform, contrast-based text detection, and machine learning classifiers for accurate news video format analysis.
Findings
ELM classifier achieved 77.38% accuracy
SVM classifier achieved 96.5% accuracy
Band detection with a Jaccard Index of 0.8138
Abstract
Over the past few years, there has been an astounding growth in the number of news channels as well as the amount of broadcast news video data. As a result, it is imperative that automated methods need to be developed in order to effectively summarize and store this voluminous data. Format detection of news videos plays an important role in news video analysis. Our problem involves building a robust and versatile news format detector, which identifies the different band elements in a news frame. Probabilistic progressive Hough transform has been used for the detection of band edges. The detected bands are classified as natural images, computer generated graphics (non-text) and text bands. A contrast based text detector has been used to identify the text regions from news frames. Two classifers have been trained and evaluated for the labeling of the detected bands as natural or…
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Taxonomy
TopicsVideo Analysis and Summarization · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques
MethodsSupport Vector Machine
