Feature hashing

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

Hashing methods for temporal data

Abstract: External dynamic hashing has been used in traditional database systems as a fast method for answering membership queries. Given a dynamic set S of objects, a membership query asks whether an object with identity k is in (the most current state of) S. This paper addresses the more general problem of temporal hashing. In this setting, changes to the dynamic set are time-stamped and the membership query has a temporal predicate, as in: "Find whether object with identity k was in set S at time t". We present an efficient solution for this problem that takes an ephemeral hashing scheme and makes it partially persistent. Our solution, also termed partially persistent hashing, uses a space that is linear on the total number of changes in the evolution of set S and has a small {O[log/sub B/(n/B)]} query overhead. An experimental comparison of partially persistent hashing with various straightforward approaches (like external linear hashing, the multi-version B-tree and the R*-tree) shows that it provides the faster membership query response time. Partially persistent hashing should be seen as an extension of traditional external dynamic hashing in a temporal environment. It is independent of the ephemeral dynamic hashing scheme used; while this paper concentrates on linear hashing, the methodology applies to other dynamic hashing schemes as well.

Scalable Discrete Supervised Multimedia Hash Learning With Clustering

Abstract: The hashing method maps similar data of various types to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low-storage cost and fast retrieval speed However, the existing limitations make the present algorithms difficult to deal with for large-scale data sets: 1) discrete constraints are involved in the learning of the hash function and 2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting in both time and space complexity greater than $O(n^{2})$ To address these issues, we propose a novel discrete supervised hash learning framework that can be scalable to large-scale data sets of various types First, the discrete learning procedure is decomposed into a binary classifier learning scheme and binary codes learning scheme, which makes the learning procedure more efficient Second, by adopting the asymmetric low-rank matrix factorization , we propose the fast clustering-based batch coordinate descent method, such that the time and space complexity are reduced to $O(n)$ The proposed framework also provides a flexible paradigm to incorporate with arbitrary hash function, including deep neural networks and kernel methods, as well as any types of data to hash, including images and videos Experiments on large-scale data sets demonstrate that the proposed method is superior or comparable with the state-of-the-art hashing algorithms

A robust and secure perceptual hashing system based on a quantization step analysis

Abstract: Perceptual hashing is conventionally used for content identification and authentication. It has applications in database content search, watermarking and image retrieval. Most countermeasures proposed in the literature generally focus on the feature extraction stage to get robust features to authenticate the image, but few studies address the perceptual hashing security achieved by a cryptographic module. When a cryptographic module is employed [1], additional information must be sent to adjust the quantization step. In the perceptual hashing field, we believe that a perceptual hashing system must be robust, secure and generate a final perceptual hash of fixed length. This kind of system should send only the final perceptual hash to the receiver via a secure channel without sending any additional information that would increase the storage space cost and decrease the security. For all of these reasons, in this paper, we propose a theoretical analysis of full perceptual hashing systems that use a quantization module followed by a crypto-compression module. The proposed theoretical analysis is based on a study of the behavior of the extracted features in response to content-preserving/content-changing manipulations that are modeled by Gaussian noise. We then introduce a proposed perceptual hashing scheme based on this theoretical analysis. Finally, several experiments are conducted to validate our approach, by applying Gaussian noise, JPEG compression and low-pass filtering.