Feature normalization across different video sources is performed to ensure that the features extracted from videos are comparable, regardless of the source variations. Videos can differ in terms of resolution, lighting, frame rates, and even encoding formats, which can affect the consistency of the features. Normalization techniques are applied to adjust these discrepancies so that machine learning models can learn from data more effectively.
The most common method of feature normalization is statistical scaling, where features are standardized to have a mean of zero and a standard deviation of one. For example, if you extract color histograms or motion vectors from different videos, these features may differ in scale. To normalize these, you can calculate the mean and standard deviation of each feature across a training set and then adjust all extracted features accordingly. This makes it easier for algorithms to learn patterns without being biased by the scale of any particular video source.
Another approach is min-max scaling, which rescales features to a fixed range, usually [0, 1]. This method is particularly useful when dealing with features that have different ranges. For instance, if you're analyzing the intensity of video frames, one video might have pixel values ranging from 0 to 255, while another might only range from 0 to 128. By applying min-max scaling, you can transform all features to be bounded within the same range, thus facilitating comparisons and ensuring a more robust performance of machine learning models across varied video datasets.