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Unsupervised Learning of Object Models from Video Sequences
M. Leordeanu and R. Collins
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), Vol. 1, June, 2005, pp. 1142 - 1149.

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We develop an efficient algorithm for unsupervised learning of object models as constellations of features, from low resolution video sequences. The input images typically contain single or multiple objects that change in pose, scale and degree of occlusion. Also, the objects can move significantly between consecutive frames. The content of an input sequence is unlabeled so the learner has to cluster the data based on the data's implicit coherence over time and space. Our approach takes advantage of the dependent pairwise co-occurrences of objects' features within local neighborhoods vs. the independent behavior of unrelated features. We couple or decouple pairs of features based on a probabilistic interpretation of their pairwise statistics and then extract objects as connected components of features.

Number of pages: 8

M. Leordeanu and R. Collins, "Unsupervised Learning of Object Models from Video Sequences," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), Vol. 1, June, 2005, pp. 1142 - 1149.

@inproceedings{Leordeanu_2005_5230,
   author = "Marius Leordeanu and Robert Collins",
   title = "Unsupervised Learning of Object Models from Video Sequences",
   booktitle = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05)",
   month = "June",
   year = "2005",
   volume = "1",
   pages = "1142 - 1149"
}