  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 artificial - Support Vector
Machine (SVM) Classifer with RBF kernel, and Extreme Learning Machine (ELM)
classifier. The classifiers have been trained on a dataset of 6000 images (3000
images of each class). The ELM classifier reports a balanced accuracy of
77.38%, while the SVM classifier outperforms it with a balanced accuracy of
96.5% using 10-fold cross-validation. The detected bands which have been
fragmented due to the presence of gradients in the image have been merged using
a three-tier hierarchical reasoning model. The bands were detected with a
Jaccard Index of 0.8138, when compared to manually marked ground truth data. We
have also presented an extensive literature review of previous work done
towards news videos format detection, element band classification, and
associative reasoning.
