Toward a computational theory of shape: an overview
ECCV 90 Proceedings of the first european conference on Computer vision
International Journal of Computer Vision
Euler Spiral for Shape Completion
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
On the Intrinsic Reconstruction of Shape from Its Symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
From Aardvark to Zorro: A Benchmark for Mammal Image Classification
International Journal of Computer Vision
Unsupervised Category Modeling, Recognition, and Segmentation in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Category independent object proposals
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The demise of "segmentation-then-recognition" strategy led to a paradigm shift toward feature-based discriminative recognition with significant success. However, increased complexity in multi-class datasets reveals that local low-level features may not be sufficiently discriminative, requiring the construction and use of more complex structural features which are necessarily category independent. The paper proposes a bottom-up procedure for generating fragment features which are intended to be object part hypotheses. Suggesting that the demise of segmentation to generate a representation suitable for recognition was due to prematurely committing to a grouping option in the face of ambiguities, the proposed framework considers and tracks multiple alternate grouping options. This approach is made tractable by (i) using a medial fragment representation which allows for the simultaneous use of multiple cues, (ii) a set of transforms to effect grouping operations, (iii) a containment graph representation which avoids duplicate consideration of possibilities, and the estimation of the likelihood of a grouping sequence to retain only plausible groupings. The resulting hypotheses are evaluated intrinsically by measuring their ability to represent objects with a few fragments. They are also evaluated by comparison to algorithms which aim to generate full object segments, with results that match or exceed the state of art, thus demonstrating the suitability of the proposed mid-level representation.