What is the next step of binary features?

Various binary features have been recently proposed in literature, aiming at improving the computational efficiency and storage efficiency of image retrieval applications. However, the most common way of using binary features is voting strategy based on brute-force matching, since binary features are discrete data points distributed in Hamming space, so that models based on clustering such as BoW are unsuitable for them. Although indexing mechanism substantially decreases the time cost, the brute-force matching strategy becomes a bottleneck that restricts the performance of binary features. To address this issue, we propose a simple but effective method, namely COIP (Coding by Order-independent Projection), which projects binary features into a binary code of limited bits. As a result, each image is represented by one single binary code that can be indexed for computational and storage efficiency. We prove that the similarity between the COIP codes of two images with probability proportional to the ratio of their matched features. A comprehensive evaluation with several state-of-the-art binary features is performed on benchmark dataset. Experimental results reveal that for binary feature based image retrieval, our approach improves the storage/time efficiency by one/two orders of magnitude, while the retrieval performance remains almost unchanged.