Key-Places Detection and Clustering in Movies Using Latent Aspects

We describe a new method to find and cluster recurrent keyplaces in a movie. It consists of an unsupervised classification of shots that are taking place in the same physical location (key-place). Our approach is based on finding links between key-frames belonging to a same keyplace. We use a probabilistic latent space model over the possible match points between the image sets. This allows extracting significant groups of local descriptor matches that may represent characteristic elements of a key-place. A preliminary test on a full-length movie gives a recognition rate of 78.0% on the key-places clustering.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Maguelonne Héritier, Samuel Foucher, Langis Gagnon
• Keywords: ICIP 2007, Image Processing, Possible Match Points, Probabilistic Latent Space, Unsupervised Classification