Segmentation of TV news broadcast into semantically meaningful stories is an essential pre-requisite for a wide range of video analytics applications. In this work we have introduced a hybrid approach for news story segmentation based on conditional random fields (CRFs). The story boundary detection problem is converted into a shot classification problem by classifying video shots into either of the four categories. These are start shot, end shot and middle shots of a story or single shot story. To achieve this classification, we have introduced two new features. These are overlay text based semantic similarity and grid-wise edge orientation histogram. The first feature measures the semantic similarity between video shots by linking them through a set of web news articles. We use overlay text with their relevance as weight to link a set of articles with the video shots. The second feature captures the variations in presentation formats. The CRF model effectively combines these two features to model the news stories. Experimental results on approximately 50 hours of news videos demonstrates the efficiency of the proposed features. We were able to achieve an F1 score of 81% with our proposed features.

Multiple kernel learning methods combine a set of base kernels to produce an optimal one for a certain classification or regression problem. But selecting a set of base kernels from a plethora of kernels is not automated. We provide a criteria to select efficient base kernels. Automating the selection process of efficient base kernel requires less time and effort than manually selecting them. However, learning the weights in the ratio of which the selected kernels are to be combined is still a costly process. To calculate these combination weights, we first evaluate the efficiency of a kernel on the basis of two parameters viz. the Trace and Alignment of the kernel matrix using data envelopment analysis. A base kernel can be selected if its efficiency is 100%. After selecting a set of most efficient kernels, we combine them in the proportion to their efficiencies. Also, we want to control the complexity of the model by the method of data selection. We use feature vector selection method to cast data points to a limited number of features and then apply classical algorithms to solve our classification problems.

Television news channels broadcast different kinds of content like debates, interviews, commercials along with news presentations. Real-time detection of these news programs or their retrieval from large volumes of stored broadcast videos is a challenging problem and is a necessary first step for broadcast analytics. News program detection is even harder in Indian context where closed caption text or program markers are not provided by TV news channels (not mandated by law). We propose a two-stage approach to classify news video segments. First, broadcast video shots are classified with multiple labels based on a set of audio-visual features. Second, sequences of these shot features are modeled to detect news programs. Another contribution of this work is the construction of a dataset of 120 hours of shot categories and news programs from Indian English news channels. We have experimented with SVM, HMM and CRF based classifiers and achieved a F1 score of 99% in detecting news programs while experimenting on our dataset.