Corpus Generation and Analysis: Incorporating Audio Data Towards Curbing Missing Information.

As video data becomes widely available, it is crucial that these videos are properly annotated for effective search, mining and retrieval purposes. Significant work has been done to explore natural language description as it can provide better understanding of the video content. Ideally, a summary should be informative and accurate in order for the users to have good understanding of the video content. An experiment has been conducted to evaluate the impact of audio information towards natural language summary annotations of a video content. The experiment proved that although events and human activities can be captured using visual features alone, key information of the video content would be missing without the audio information. Thus, future work on natural language summary generation should incorporate both visual and audio data to curb missing and erroneous information.