Evaluation of Hashing Methods Performance on Binary Feature Descriptors

In this paper we evaluate performance of data-dependent hashing methods on binary data. The goal is to find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors. A representative sample of recent unsupervised, semi-supervised and supervised hashing methods was experimentally evaluated on large datasets of labelled binary FREAK feature descriptors

Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space

Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search

Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset. Existing methods always neglect that real-world remote sensing data lies on a low-dimensional manifold embedded in high-dimensional ambient space. Unlike previous methods, this article proposes to learn the consensus compact codes in a view-specific low-dimensional subspace. Furthermore, we have added a hyperparameter learnable module to avoid complex parameter tuning. In order to prove the effectiveness of our method, we carried out experiments on three widely used remote sensing data sets and compared them with seven state-of-the-art methods. Extensive experiments show that the proposed method can achieve competitive results compared to the other method.Comment: 5 pages,icassp accepte

The Immutable Blockchain Confronts the Unstoppable GDPR

The notion that privacy is dispensable and should be sacrificed in exchange for internet access is misguided. In fact, privacy laws are flourishing, highlighting the significance of safeguarding personal information in the digital age. It is crucial to recognize that privacy is not merely a luxury, but a fundamental right that should be upheld, even in the context of online activities. In the ever-evolving landscape of technology, the collision between privacy and innovation becomes increasingly apparent. This paper delves into the intriguing convergence of the General Data Protection Regulation (GDPR) and blockchain technology, unraveling pivotal issues that arise from this intersection. Firstly, this article explores the compatibility of encryption and hashing mechanisms on the blockchain with GDPR\u27s stringent criteria for anonymous data is analyzed, illuminating the ongoing debates in this area. Secondly, this article considers the challenges to conventional notions of centralized control caused by the intricate task of identifying data controllers within decentralized blockchains, particularly in the dynamic realm of public blockchains. Lastly, this article addresses the perplexing question of exercising data subject rights in decentralized environments, where the immutability of data poses significant hurdles to the practical implementation of rights such as erasure and rectification. Through a comprehensive analysis of these issues, this article emphasizes the crucial need for the harmonious coexistence of privacy principles and technological advancements