Global temporal descriptor

In this algorithm, a global intensity is defined as the sum of all intensities of all pixels weighted along all the video. Thus, an identity for a video sample can be constructed on the basis of the length of the video and the pixel intensities throughout.

The global intensity a(t) is defined as:

${\displaystyle a(t)=\sum _{i=1}^{N}K(i)(I(i,t-1))^{2}}$

Where k is the weighting of the image, I is the image, and N is the number of pixels in the image.

Global ordinal measurement descriptor

In this algorithm, the video is divided in N blocks, sorted by gray level. Then it's possible to create a vector describing the average gray level of each block.

With these average levels it is possible to create a new vector S(t), the video's signature:

${\displaystyle S(t)=(r_{1},r_{2},\cdots ,r_{N})}$

To compare two videos, the algorithm defines a D(t) representing the similarity between both.

${\displaystyle D(t)={\frac {1}{T}}\sum _{1=t-{\frac {T}{2}}}^{t+{\frac {T}{2}}}{\begin{vmatrix}R(i)-C(i)\end{vmatrix}}}$

The value returned by D(t) helps determine whether the video in question is a copy.^{[clarification needed]}

Ordinal and Temporal Descriptors

This technique was proposed by L.Chen and F. Stentiford. A measurement of dissimilarity is made by combining the two aforementioned algorithms, Global temporal descriptors and Global ordinal measurement descriptors, in time and space.^{[clarification needed]}

TMK+PDQF

In 2019, Facebook open sourced TMK+PDQF,^[2] part of a suite of tools used at Facebook to detect harmful content. It generates a signature of a whole video, and can easily handle changes in format or added watermarks, but is less tolerant of cropping or clipping.^[3]

AJ

Described by A. Joly et al., this algorithm is an improvement of Harris' Interest Points detector.^{[clarification needed (what is this?)]} This technique suggests that in many videos a significant number of frames are almost identical, so it is more efficient to test not every frame but just those depicting a significant amount of motion.

ViCopT

ViCopT uses the interest points from each image to define a signature of the whole video. In every image, the algorithms identifies and defines two parts: the background, a set of static elements along a temporal sequence, and the motion, persistent points changing positions throughout the video.

Space Time Interest Points (STIP)

This algorithm was developed by I. Laptev and T.Lindeberg. It uses the interest points technique along the space and time to define the video signature, and creates a 34th-dimension vector that stores this signature.^{[clarification needed]}