There is a lot of video content available on the internet today, and that number is only going to increase with time. This sustained increase in video content has made searching for a video or desired information within a particular video very difficult. Video annotation easily solves this problem.
Annotation which refers to the attachment of data to some other piece of data (or adding data to data) greatly simplifies video access. You basically extract critical information about a video and add this information to the video in a different data form to help with categorization, browsing, retrieval, searching, analysis, and comparison of videos.
The following are 3 common annotation techniques you can start using today to make your videos and specific content within the videos easier to find;
Free text description
This is perhaps the most popular annotation technique, especially on search engines. What you do is add text describing the video and submit it to search engines alongside the video. You can also use this technique to annotate YouTube videos and other videos that aren’t necessarily designed for search engines. As you submit the video, find out if there is an option to submit descriptive text as well.
The main advantage of this form of annotation is that it makes video retrieval easier. If a user is searching the video in a library, all they have to do is enter the keywords in the description. In social networks such as Facebook and search engines such as Google, if a user searches certain keywords, say football, alongside the text results, Google will display videos that have the keyword “football” in their descriptions.
Annotations based on text in the video
Another option you’ve probably seen in movies and long videos is text within the video sequence. For instance, as you watch a promotional video from a supermarket chain, you may notice that some of the words spoken are also written as text within the video exactly when the words are spoken. These are called collateral text.
A key advantage of collateral text is that they allow for keywords and potentially richer representations to be extracted. Using a snipping tool, you capture the most important parts of your video and use text to describe or transcribe that part. The result is easier filtering and searching.
Annotation based on rule learning
Finally, this is a little complex compared to the first two but is still an excellent option to consider if you want to make your videos easier to search and understand. Essentially, visual features are directly extracted from the video and these low-level features used to provide useful annotation.
There are rules that guide what can be extracted automatically from visual data and how such data can be interpreted depending on the given situation. But, if done correctly, the method allows users as well as search engines to derive hidden knowledge that can help in understanding videos.
Two other annotation techniques out there are ontology-based annotation and annotation based on machine learning. But the three methods discussed above are the easiest to master.