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Abstract: Learning-based hashing algorithms are ``hot topics' because they can greatlyincrease the scale at which existing methods operate. In this paper, we proposea new learning-based hashing method called ``fast supervised discrete hashing'(FSDH) based on ``supervised discrete hashing' (SDH). Regressing the trainingexamples (or hash code) to the corresponding class labels is widely used inordinary least squares regression. Rather than adopting this method, FSDH usesa very simple yet effective regression of the class labels of training examplesto the corresponding hash code to accelerate the algorithm. To the best of ourknowledge, this strategy has not previously been used for hashing. TraditionalSDH decomposes the optimization into three sub-problems, with the most criticalsub-problem - discrete optimization for binary hash codes - solved usingiterative discrete cyclic coordinate descent (DCC), which is time-consuming.However, FSDH has a closed-form solution and only requires a single rather thaniterative hash code-solving step, which is highly efficient. Furthermore, FSDHis usually faster than SDH for solving the projection matrix for least squaresregression, making FSDH generally faster than SDH. For example, our resultsshow that FSDH is about 12-times faster than SDH when the number of hashingbits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster thanFastHash when the number of hashing bits is 64 on the MNIST>loading...
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