Wavelet, active basis, and shape script: a tour in the sparse land
Proceedings of the international conference on Multimedia information retrieval
A Hierarchical and Contextual Model for Aerial Image Parsing
International Journal of Computer Vision
Learning Active Basis Model for Object Detection and Recognition
International Journal of Computer Vision
Discovering multipart appearance models from captioned images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A coarse-to-fine taxonomy of constellations for fast multi-class object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Figure-ground image segmentation helps weakly-supervised learning of objects
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
International Journal of Computer Vision
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Predicate Logic Based Image Grammars for Complex Pattern Recognition
International Journal of Computer Vision
A hierarchical recursive partial active basis model
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Transformation equivariant Boltzmann machines
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
A probabilistic model for component-based shape synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Discovering hierarchical object models from captioned images
Computer Vision and Image Understanding
Learning a generative model of images by factoring appearance and shape
Neural Computation
Multiscale edge detection based on Gaussian smoothing and edge tracking
Knowledge-Based Systems
One-Class multiple instance learning via robust PCA for common object discovery
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We describe a new method for unsupervised structure learning of a hierarchical compositional model (HCM) for deformable objects. The learning is unsupervised in the sense that we are given a training dataset of images containing the object in cluttered backgrounds but we do not know the position or boundary of the object. The structure learning is performed by a bottom-up and top-down process. The bottom-up process is a novel form of hierarchical clustering which recursively composes proposals for simple structures to generate proposals for more complex structures. We combine standard clustering with the suspicious coincidence principle and the competitive exclusion principle to prune the number of proposals to a practical number and avoid an exponential explosion of possible structures. The hierarchical clustering stops automatically, when it fails to generate new proposals, and outputs a proposal for the object model. The top-down process validates the proposals and fills in missing elements. We tested our approach by using it to learn a hierarchical compositional model for parsing and segmenting horses on Weizmann dataset. We show that the resulting model is comparable with (or better than) alternative methods. The versatility of our approach is demonstrated by learning models for other objects (e.g., faces, pianos, butterflies, monitors, etc.). It is worth noting that the low-levels of the object hierarchies automatically learn generic image features while the higher levels learn object specific features.