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Locality sensitive hashing (LSH) has been used extensively as a basis for many data retrieval applications. However, previous approaches, such as random projection and multi-probe hashing, may exhibit high query complexity of up to Θ(n) when the underlying data distribution is highly skewed. This is due to the imbalance in the number of data stored per each(More)
We propose a novel method for identifying and classifying motions that offers significantly reduced computational cost as compared to deep convolutional neural network systems with comparable performance. Our new approach is inspired by the information processing network architecture of biological visual processing systems, whereby spatial pyramid kernel(More)
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