Patent Document ID: 9152881
Application ID: 14026295

Base Claim:
1. A computer-implemented method, comprising: learning, by a computing system, a sparse overcomplete feature dictionary for classifying and/or clustering a remote sensing image dataset; building, by the computing system, a local sparse representation of the image dataset using the learned sparse overcomplete feature dictionary; and applying, by the computing, system, a local maximum pooling operation on the local sparse representation to produce a translation-tolerant representation of the image dataset.

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Claim 2:
2. The computer-implemented method of claim 1 , wherein the learning of the sparse overcomplete feature dictionary comprises: initializing, by the computing system, atoms φ k of a feature dictionary Φ either by imprinting a set of unlabeled patches x, or by initializing φ k using random vectors; for each unlabeled patch in x, seeking, by the computing system, a coefficient vector y such that y is sparse and Φy approximates x; finding, by the computing system, an approximate solution for y; and updating Φ, by the computing system, using a learning rule.