Building a Content-Based Search Engine II: Extracting Feature Vectors

By Prosyscom
In March 9, 2018


Since we are interested in visual similarity between two videos, our goal is to extract a set of vectors, each of which describes certain local visual properties of the video numerically. This process can be depicted visually as follows:

Extracting feature vectors from videos

Extracting feature vectors from videos

We first select a certain amount of sample frames from the video (e.g. 10 frames per second). For each of these frames, we select a fixed amount of equidistant sample pixels. Finally, for each sample pixel, we compute an 8-dimensional Euclidean vector $(x, y, L, a, b, chi, eta, t)$ describing the visual appearance of the pixel and its context. The choice of this vector is just a suggestion and it isn’t necessary to include all of features presented here.


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