Scalable Image Search with Reliable Binary Code

In many existing image retrieval algorithms, Bag-of-Words (BoW) model has been widely adopted for image representation. To achieve accurate indexing and efficient retrieval, local features such as the SIFT descriptor are extracted and quantized to visual words. One of the most popular quantization scheme is scalar quantization, which generates binary signature with an empirical threshold value. However, such binarization strategy inevitably suffers from the quantization loss induced by each quantized bit and impairs the effectiveness of search performance. In this paper, we investigate the reliability of each bit in scalar quantization and propose a novel reliable binary SIFT feature. We move one step ahead to incorporate the reliability in both index word expansion and feature similarity. Our proposed approach not only accelerates the search speed by narrowing search space, but also improves the retrieval accuracy by alleviating the impact of unreliable quantized bits. Experimental results demonstrate that the proposed approach achieves significant improvement in retrieval efficiency and accuracy.