Nowadays, Medical Imaging is emerged well with computer applications. The diagnosis process is fully automated; based on the medical image, cases are reasoned to examine the causes such as Cancer Prediction, Age Estimation, and Injuries and so on. Automatic Image Annotation (AIA) is widely used in Medical field. Generally, the images are the retrieved using CBIR (Content-based Image Retrieval) ...

Taking into account the information security problem in medical image, the paper put forward a new robust zerowatermarking algorithm with three-dimensional (3D) discrete Fourier transform (DFT) and perceptual hashing. The medical volume data was made by 3D DFT. The zero-watermarking algorithm selected the real part of 3D DFT coefficients (4*4*4) to generate the watermarking extraction key seque...

The hashing methods have been widely used for efficient similarity retrieval on large scale image datasets. The traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing method...

There is a growing trend in studying deep hashing methods for content-based image retrieval (CBIR), where hash functions and binary codes are learnt using deep convolutional neural networks and then the binary codes can be used to do approximate nearest neighbor (ANN) search. All the existing deep hashing papers report their methods’ superior performance over the traditional hashing methods acc...

In this paper, we proposed a more robust supervised hashing framework based on the Cauchy loss function and Supervised Discrete Hashing (SDH) called Robust (RSDH), which can learn subspace consisted of binary codes. The is used to measure error between label matrix product decomposed matrices. RSDH not only reduce outliers noise codes, but also achieve satisfactory retrieval effect. Image exper...

This paper presents a content-based image retrieval technique based on interest points matching and geometric hashing. We estimate points with significant luminance variations as interest points. A small region around the interest point is located as an image patch. Low-level features are extracted to describe each image patch. To provide geometric invariant image matching, we index the image p...

This is a new technology to support scalable content-based image retrieval (CBIR]), hashing has been recently been focused and future directions of research domain. In this paper, we propose a unique unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea emphatically extracts the rich s...

Deep supervised hashing (DSH), which combines binary learning and convolutional neural network, has attracted considerable research interests and achieved promising performance for highly efficient image retrieval. In this paper, we show that the widely used loss functions, pair-wise loss and triplet loss, suffer from the trivial solution problem and usually lead to highly correlated bits in pr...