Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes compact storage and efficient semantic retrieval. We propose an unsupervised deep layer called Bi-Half Net that maximizes entropy of the codes. Entropy maximal when both possible values bit are uniformly (half-half) distributed. ...

Multimedia fingerprinting, also known as robust or perceptual hashing, aims at representing multimedia signals through compact and perceptually significant descriptors (hash values). In this paper, we examine the probability of collision of a certain general class of robust hashing systems that, in its binary alphabet version, encompasses a number of existing robust audio hashing algorithms. Ou...

Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming with low dimension. However, high-speed retrieval through binary code has certain degree of reduction accuracy compared to traditional methods. We have noticed that multi-view methods can well preserve the diverse characteris...

Nowadays, people’s lives are filled with a huge amount of picture information, and image retrieval tasks widely needed. Deep hashing methods extensively used to manage such demands due their rate memory consumption. The problem conventional deep techniques, however, is that high dimensional semantic content in the cannot be effectively articulated insufficient unbalanced feature extraction. Thi...

Image hashing is the process of associating a short vector of bits to an image. The resulting summaries are useful in many applications including image indexing, image authentication and pattern recognition. These hashes need to be invariant under transformations of the image that result in similar visual content, but should drastically differ for conceptually distinct contents. This paper prop...

Advanced hashing technique is essential to facilitate effective organization and retrieval over online image collections, where the contents are frequently changed. Traditional multi-view hashing methods based on batch-based learning generally leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising per...