To provide high quality content-based search services over huge volume of image collections, both efficiency and effectiveness are important issues. Advanced indexing structure is essential to scale the big data space and facilitate accurate search. The most naive approach for CBIR is to sequentially compare query image with each sample stored in the database. Its linear complexity leads to the...

In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach capable to overcome these shortcomings in certain setups. The consideration is based on both achievable rate and probability of error. We use a fact th...

We introduce a new transform called the randlet transform and explore applications to universal perceptual image hashing (key-based randomized digests) and image identification. Our transform yields robust signal representations that are nearly invariant to several perceptually insignificant transformations (attacks, intentional or otherwise). Our signal representation is hard to guess without ...

Hashing-based methods seek compact and efficient binary codes that preserve the neighborhood structure in the original data space. For most existing hashing methods, an image is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to get the compact binary vector. Most of the hand-crafted features just encode the low-level information of ...

Hashing techniques have become very popular to solve the content-based image retrieval problem in gigantic image databases because they allow to represent feature vectors using compact binary codes. Binary codes provide speed and are memory-efficient. Different approaches have been taken by researchers, some of them based on the Spectral Hashing objective function, among these the recently prop...

Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks. Most existing hashing methods first encode the images as a vector of hand-crafted features followed by a separate binarization step to generate hash codes. This two-stage process may produce sub-optimal encoding. In this paper, for the first time, we propose a ...

The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global representations, e.g., GIST [3], which lack the analysis of the intrinsic geometric property of local features and heavily limit the effectiveness of the hash code. In t...

Image hashing is an effective approach to image authentication. In this paper, a geometric distortioninvariant image hashing scheme, which can detect and locate the tampering, is proposed. The reported scheme performs Polar Complex Exponential Transform (PCET) on an image, and then the magnitudes and the phases of PCETs are combined to generate a set of rotation and scaling invariant features. ...

The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imager...