Video retrieval based on object discovery
Computer Vision and Image Understanding
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
Unsupervised identification of multiple objects of interest from multiple images: dISCOVER
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Affine warp propagation for fast simultaneous modelling and tracking of articulated objects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Robust non-rigid object tracking using point distribution manifolds
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Unsupervised Learning for Graph Matching
International Journal of Computer Vision
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We develop an efficient algorithm for unsupervised learningof 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.