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Representing images by compact hash codes is an attractive approach forlarge-scale content-based image retrieval. In most state-of-the-arthashing-based image retrieval systems, for each image, local descriptors arefirst aggregated as a global representation vector. This global vector is thensubjected to a hashing function to generate a binary hash code. In previousworks, the aggregating and the hashing processes are designed independently.Hence these frameworks may generate suboptimal hash codes. In this paper, wefirst propose a novel unsupervised hashing framework in which featureaggregating and hashing are designed simultaneously and optimized jointly.Specifically, our joint optimization generates aggregated representations thatcan be better reconstructed by some binary codes. This leads to morediscriminative binary hash codes and improved retrieval accuracy. In addition,the proposed method is flexible. It can be extended for supervised hashing.When the data label is available, the framework can be adapted to learn binarycodes which minimize the reconstruction loss w.r.t. label vectors. Furthermore,we also propose a fast version of the state-of-the-art hashing method BinaryAutoencoder to be used in our proposed frameworks. Extensive experiments onbenchmark datasets under various settings show that the proposed methodsoutperform state-of-the-art unsupervised and supervised hashing methods.

Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning