Triple-Bit Quantization with Asymmetric Distance for Image Content Security

With the rapid growth of the number of images on the Internet, it has become more necessary to ensure the content security of images. The key problem is retrieving relevant images from the large database. Binary embedding is an effective way to improve the efficiency of calculating similarities for image content security as binary code is storage efficient and fast to compute. It tries to convert real-valued signatures into binary codes while preserving similarity of the original data, and most binary embedding methods quantize each projected dimension to one bit (presented as 0/1). As a consequence, it greatly decreases the discriminability of original signatures. In this paper, we first propose a novel triple-bit quantization strategy to solve the problem by assigning 3-bit to each dimension. Then, asymmetric distance algorithm is applied to re-rank candidates obtained from Hamming space for the final nearest neighbors. For simplicity, we call the framework triple-bit quantization with asymmetric distance (TBAD). The inherence of TBAD is combining the best of binary codes and real-valued signatures to get nearest neighbors quickly and concisely. Moreover, TBAD is applicable to a wide variety of embedding techniques. Experimental comparisons on BIGANN set show that the proposed method can achieve remarkable improvement in query accuracy compared to original binary embedding methods.