Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.

Hash Bit Selection for Nearest Neighbor Search

Abstract: To overcome the barrier of storage and computation when dealing with gigantic-scale data sets, compact hashing has been studied extensively to approximate the nearest neighbor search. Despite the recent advances, critical design issues remain open in how to select the right features, hashing algorithms, and/or parameter settings. In this paper, we address these by posing an optimal hash bit selection problem, in which an optimal subset of hash bits are selected from a pool of candidate bits generated by different features, algorithms, or parameters. Inspired by the optimization criteria used in existing hashing algorithms, we adopt the bit reliability and their complementarity as the selection criteria that can be carefully tailored for hashing performance in different tasks. Then, the bit selection solution is discovered by finding the best tradeoff between search accuracy and time using a modified dynamic programming method. To further reduce the computational complexity, we employ the pairwise relationship among hash bits to approximate the high-order independence property, and formulate it as an efficient quadratic programming method that is theoretically equivalent to the normalized dominant set problem in a vertex- and edge-weighted graph. Extensive large-scale experiments have been conducted under several important application scenarios of hash techniques, where our bit selection framework can achieve superior performance over both the naive selection methods and the state-of-the-art hashing algorithms, with significant accuracy gains ranging from 10% to 50%, relatively.

Learning to hash on partial multi-modal data

Abstract: Hashing approach becomes popular for fast similarity search in many large scale applications. Real world data are usually with multiple modalities or having different representations from multiple sources. Various hashing methods have been proposed to generate compact binary codes from multi-modal data. However, most existing multimodal hashing techniques assume that each data example appears in all modalities, or at least there is one modality containing all data examples. But in real applications, it is often the case that every modality suffers from the missing of some data and therefore results in many partial examples, i.e., examples with some modalities missing. In this paper, we present a novel hashing approach to deal with Partial Multi-Modal data. In particular, the hashing codes are learned by simultaneously ensuring the data consistency among different modalities via latent subspace learning, and preserving data similarity within the same modality through graph Laplacian. We then further improve the codes via orthogonal rotation based on the orthogonal invariant property of our formulation. Experiments on two multi-modal datasets demonstrate the superior performance of the proposed approach over several state-of-the-art multi-modal hashing methods.

Robust Hashing Based on Quaternion Zernike Moments for Image Authentication

Abstract: The reliability and security of multimedia contents in transmission, communications, storage, and usage have attracted special attention. Robust image hashing, also referred to as perceptual image hashing, is widely applied in multimedia authentication and forensics, image retrieval, image indexing, and digital image watermarking. In this work, a novel robust image hashing method based on quaternion Zernike moments (QZMs) is proposed. QZMs offer a sound way to jointly deal with the three channels of color images without discarding chrominance information; the generated hash is thus shorter than the hash of three channels separately processing. The proposed approach's performance was evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash in length that is robust to most common image content-preserving manipulations like JPEG compression, filtering, noise, scaling, and large angle rotation operations.