With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.
Single image super-resolution (SISR), which aims at obtaining a high-resolution image from a single low-resolution image, is a classical problem in computer vision. In this paper, we address this problem based on a deep learning method with residual learning in an end-to-end manner. We propose a novel residual-network architecture, Residual networks of Residual networks (RoR), to promote the learning capability of residual networks for SISR. In residual network, the signal can be directly propagated from one unit to any other units in both forward and backward passes when using identity mapping as the skip connections. Based on it, we add level-wise connections upon original residual networks, to dig the optimization ability of residual networks. Our experiments demonstrate the effectiveness and versatility of RoR, it can get a faster convergence speed and gain higher resolution accuracy from considerably increased depth.