Feature weighting methods for abstract features applicable to motion based video indexing

Content based labels, associated with image sequences in contemporary video indexing methods, can be textual, numerical as well as abstract, including colour-histograms and motion co-occurrence matrices. Abstract features or indices are not explicitly numeric entities but rather are composed of numeric entities. When multiple abstract features are involved, distance metrics between image sequences need to be weighted. Most feature weighting methods in the literature assume that the space is numeric (either discrete or continuous) and so not applicable to abstract feature weighting. This paper elaborates some feature weighting methods applicable to abstract features and both binary (feature selection) and real-valued weighting methods are discussed. The performance of different feature selection and weighting methods are provided and a comparative study based on motion classification-experiments is presented.