Kernelized sparse hashing for scalable image retrieval

Recently, hashing has been widely applied to large scale image retrieval applications due to its appealing query speed and low storage cost. The key idea of hashing is to learn a hash function that maps high dimensional data into compact binary codes while preserving the similarity structure in the original feature space. In this paper, we propose a new method called the Kernelized Sparse Hashing, which generates sparse hash codes with ℓ1 and non-negative regularizations. Compared to traditional hashing methods, our method only activates a small number of relevant bits on the hash code and hence provides a more compact and interpretable representation of data. Moreover, the kernel trick is introduced to capture the nonlinear similarity of features, and the local geometrical structure of data is explicitly considered in our method to improve the retrieval accuracy. Extensive experiments on three large-scale image datasets demonstrate the superior performance of our proposed method over the examined state-of-the-art techniques.