This work presents a new video feature extraction technique based on the Generalized Hough Transform (GHT). This technique provides a way to define a similarity measure between two different frames, which establishes the basis for scene cut detection algorithms. Moreover, GHT allows to calculate the differences between two frames in terms of rotation, scale and displacement. This provides a framework for the development of global motion estimation algorithms. In addition, gradual transition detection algorithms (fades, dissolves, etc.) can also be developed. To illustrate the posibilities of this technique, a scene cut detection algorithm is also proposed. This algorithm works with MPEG video in compressed domain, achieving real time processing. An improved thresholding technique is also stated. This technique uses two different sets of similarity values making the scene cut detection algorithm perform quite well with different types of videos. The thresholding process reports two different kinds of cuts: real cuts and probable cuts. Also, it detects the location of dynamic scenes, which can be used to perform further semantic analysis. Finally, the use of the improved thresholding technique and a set of optimized parameters results in an algorithm where no human intervention is needed. Several tests have been carried out using long videos, including more than 1400 cuts. Comparison with another well-known cut detection algorithm has also been performed.