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This work addresses the problem of learning compact yet discriminative patchdescriptors within a deep learning framework. We observe that featuresextracted by convolutional layers in the pixel domain are largely complementaryto features extracted in a transformed domain. We propose a convolutionalnetwork framework for learning binary patch descriptors where pixel domainfeatures are fused with features extracted from the transformed domain. In ourframework, while convolutional and transformed features are distinctlyextracted, they are fused and provided to a single classifier which thusjointly operates on convolutional and transformed features. We experiment atmatching patches from three different datasets, showing that our feature fusionapproach outperforms multiple state-of-the-art approaches in terms of accuracy,rate, and complexity.

Feature Fusion for Robust Patch Matching With Compact Binary Descriptors