A method of segmenting a sequence of video images according to scene activity, the method comprising: defining a first series of nodes in a first multi-dimensional space, each of the first series of node corresponding to an image of the sequence of video images and its location in the first space being defined in dependence on appearance components of the respective image; defining a transformation function that maps each of the first series of nodes to a corresponding node in a second multi-dimensional space having a lower dimensionality than the first multi-dimensional space in such a way that neighbourhood relationships that exist between the nodes in the first multi-dimensional space are preserved between the respective nodes in the second multidimensional space; applying said transformation function to each of the first series of nodes to define a second series of respective nodes in the second multi-dimensional space; applying a data clustering algorithm to the second series of nodes to identify clusters of nodes within the second multi-dimensional space, characterised in that the data clustering algorithm being constrained by a measure of feature distance between a pair of clusters of nodes and a measure of temporal distance between the pair of clusters of nodes; further characterised by determining a representative image from each cluster of nodes and plotting each representative image with respect to a measure of the elapsed time of the sequence of video images to form an scene density curve indicating the underlying scene change activities, the scene density value for each representative image being inversely related to the time interval between that image's neighbouring representative images; and segmenting the sequence of video images in accordance with local minima and/or maxima of the scene density curve. defining a first series of nodes in a first multi-dimensional space, each of the first series of node corresponding to an image of the sequence of video images and its location in the first space being defined in dependence on appearance components of the respective image; defining a transformation function that maps each of the first series of nodes to a corresponding node in a second multi-dimensional space having a lower dimensionality than the first multi-dimensional space in such a way that neighbourhood relationships that exist between the nodes in the first multi-dimensional space are preserved between the respective nodes in the second multidimensional space; applying said transformation function to each of the first series of nodes to define a second series of respective nodes in the second multi-dimensional space; applying a data clustering algorithm to the second series of nodes to identify clusters of nodes within the second multi-dimensional space, characterised in that the data clustering algorithm being constrained by a measure of feature distance between a pair of clusters of nodes and a measure of temporal distance between the pair of clusters of nodes; further characterised by determining a representative image from each cluster of nodes and plotting each representative image with respect to a measure of the elapsed time of the sequence of video images to form an scene density curve indicating the underlying scene change activities, the scene density value for each representative image being inversely related to the time interval between that image's neighbouring representative images; and segmenting the sequence of video images in accordance with local minima and/or maxima of the scene density curve.