Efficient binary codes for extremely high-dimensional data
Recent advances in tackling large-scale computer vision problems have supported the use of an extremely high-dimensional descriptor to encode the image data. Under such a setting, we focus on how to efficiently carry out similarity search via employing binary codes. Observe that most of the popular high-dimensional descriptors induce feature vectors that have an implicit 2-D structure. We exploit this property to reduce the computation cost and high complexity. Specifically, our method generalizes the Iterative Quantization (ITQ) framework to handle extremely high-dimensional data in two steps. First, we restrict the dimensionality-reduction projection to a block-diagonal form and decide it by independently solving several moderate-size PCA sub-problems. Second, we replace the full rotation in ITQ with a bilinear rotation to improve the efficiency both in training and testing. Our experimental results on a large-scale dataset and comparisons with a state-of-the-art technique are promising.