Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. There has been considerable research on generating efficient image representation via the deep-network-based hashing methods. However, the issue of efficient searching in the deep representation space remains largely unsolved. To this end, we propose a simple yet efficient de...

Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing improves the quality of hash coding by exploiting the semantic similarity on data pairs and has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand-crafted or machin...

We propose an image hashing algorithm that is based on distributed compression principles. The algorithm assumes the availability of a robust feature vector extracted from the image. Then a suitable dither sequence is added to this feature vector, and the resulting dithered feature vector is compressed using distributed compression principles. We prove that the dither sequence can be used to gu...

BACKGROUND Image authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustnes...

Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussia...

Hashing techniques have been intensively investigated for large scale vision applications. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, most existing supervised hashing methods only construct similarity-preserving hash codes. Observing that semantic structures carry complementary information, we propose the idea of cotraining for ha...

Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Crossview hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multiview embedding method to tackle this problem, which inevitably causes the information loss in both i...