Patent ID: 7783106

Claim:
A method for one or both determining and distinguishing a boundary in a stream of data comprising: (a) determining one or more similarity values by comparing one or more data subsets within the stream of data with one or more neighboring data subsets, wherein the data subsets being compared are separated by an offset (L); where L is varied between 1 and a maximum offset (L); and the comparison is carried out at two or more L; (b) producing a feature vector from the similarity values, wherein the similarity values are directly combined to produce the feature vector, wherein producing the feature vector further comprises: storing the similarity values in one or more similarity matrices, where the similarity values are calculated from the pairwise comparison of one or more low level features of past, future and present data subsets by a measure selected from the group of ‘modified Chi squared’ measure, ‘Chi squared’ measure, where the modified Chi squared measure is given by D M ϰ 2 ⁡ ( X i , X j ) = ∑ p = 1 P ⁢ ⁢ ( X i ⁡ ( p ) - X j ⁡ ( p ) ) 2 ( X i ⁡ ( p ) + X j ⁡ ( p ) ) 2 , and the Chi squared measure is given by D ϰ 2 ⁡ ( X i , X j ) = ∑ p = 1 P ⁢ ⁢ ( X i ⁡ ( p ) - X j ⁡ ( p ) ) 2 ( X i ⁡ ( p ) + X j ⁡ ( p ) ) , where X i , X j are low-level features corresponding to the i th and j th respective data subsets of the stream of data and P is the dimensionality of the low level features associated with each data subset; and applying two or more kernel functions to the similarity matrices; and c) classifying the feature vector to one or both determine and distinguish boundaries.