Abstract
   We present an unsupervised technique for detecting un-
usual activity in a large video set using many simple fea-
tures. No complex activity models and no supervised feature
selections are used. We divide the video into equal length
segments and classify the extracted features into prototypes,
from which a prototype--segment co-occurrence matrix is
computed. Motivated by a similar problem in document-
keyword analysis, we seek a correspondence relationship
between prototypes and video segments which satisfies the
transitive closure constraint. We show that an important
sub-family of correspondence functions can be reduced to
co-embedding prototypes and segments to N-D Euclidean
space. We prove that an efficient, globally optimal algorithm
exists for the co-embedding problem. Experiments on vari-
ous real-life videos have validated our approach.