Classification of shape for content retrieval of images in a multimedia database

Presents a powerful and general procedure for the parametric classification of image `objects´. The parameters used are related to general `shape´ properties, but the technique can very easily be extended to other perceptually significant sets of parameters related to texture, colour, size, etc. Furthermore, the representation is such that queries can be constructed from iconic class representations, example images or even example sketches. Five perceptually meaningful shape parameters are used. These are the `circularity´, `transparency´, `aspect ratio´, `irregularity´ and the `extreme point ratio´. The values of these parameters, for a particular `object´, form a vector which represents a point in `shape´ space. The classification is performed by identifying clusters of points in this space during a training phase. Since the training data is spread over a continuum and the number of classes within the data is unknown prior to training it is appropriate to use an unsupervised classification technique