Enhanced shot boundary detection using video text information

Shot boundary detection is a pre-requisite process for content-based video indexing and retrieval, an application domain that has attracted much research and consumer attentions due to the explosive growth of video data available and accessible to users worldwide. Currently, a number of edge-based techniques have been proposed in the literature for detecting abrupt shot boundaries to avoid the influence of flashlights common in many video types, such as sports, news, entertainment, and interviews videos. However, these techniques are susceptible to miss and false detections of abrupt shot changes. Our study shows that one main reason for these errors is due to the presence of superimposed texts that are common in various video genres. To address this problem, we present in this paper an efficient method that utilizes the edge type - text-edge (edge in text region) or non-text-edge (edge in non-text region) - to reduce erroneous detection due to the sudden appearance and disappearance of superimposed text. Extensive experiments have been conducted and the results show that our proposed method, as compared with other existing methods, can obtain better performance in detecting abrupt shot boundaries and differentiating them from the effects of superimposed text.