Unsupervised Thresholds For Shape Matching

Shape recognition systems usually order a fixed number of best matches to each query, but do not address or answer the two following questions: Is a query shape in a given database ? How can we be sure that a match is correct ? This communication deals with these two key points. A database being given, with each shape S and each distance #, we associate its number of false alarms NFA(S, #), namely the expectation of the number of shapes at distance # in the database. Assume that NFA(S, #) is very small with respect to 1, and that a shape S # is found at distance # from S in the database. This match could not occur just by chance and is therefore a meaningful detection. Its explanation is usually the common origin of both shapes. Experimental evidence will show that NFA(S, #) can be predicted accurately. 1

Saxifragaceae, Crypteroniaceae, Droseraceae Hamamelidaceae, Haloragaceae

Vol. 4 (1965), Saxifragac\ue9es, [...], Punicac\ue9e