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Although the recent progress in the deep neural network has led to thedevelopment of learnable local feature descriptors, there is no explicit answerfor estimation of the necessary size of a neural network. Specifically, thelocal feature is represented in a low dimensional space, so the neural networkshould have more compact structure. The small networks required for localfeature descriptor learning may be sensitive to initial conditions and learningparameters and more likely to become trapped in local minima. In order toaddress the above problem, we introduce an adaptive pruning SiameseArchitecture based on neuron activation to learn local feature descriptors,making the network more computationally efficient with an improved recognitionrate over more complex networks. Our experiments demonstrate that our learnedlocal feature descriptors outperform the state-of-art methods in patchmatching.

Local Feature Descriptor Learning with Adaptive Siamese Network