We introduce a very large family of binary features for two-dimensional shapes. The salient ones for separating particular shapes are determined by inductive learning during the construction of classification trees. There is a feature for every possible geometric arrangement of local topographic codes. The arrangements express coarse constraints on relative angles and distances among the code locations and are nearly invariant to substantial affine and nonlinear deformations. They are also partially ordered, which makes it possible to narrow the search for informative ones at each node of the tree. Different trees correspond to different aspects of shape. They are statistically and weakly dependent due to randomization and are aggregated in a simple way. Adapting the algorithm to a shape family is then fully automatic once training samples are provided. As an illustration, we classified handwritten digits from the NIST database; the error rate was 0.7 percent.
We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information abo...
A novel approach to appearance based object recognition is introduced. The proposed method, based on matching of local image features, reliably recognises objects under very dif...
An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partia...
One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformation...
A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. I...
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