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In this paper, we propose a novel blind digital image watermarking algorithm which is resilient to many kinds of image processing attacks and geometric distortions. To embed the watermark, we first map the original watermark to another spread one aiming to fit for the image size and then embed it into the image two-level DCT coefficients we selected using a specified rule. We extract the watermark...
Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attentions. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two main steps: projection and thresholding...
In this paper, we consider the image classification problem. Unlike conventional local learning technique, a novel framework, which is based on the proposed sparsity induced neighbors (SINs) instead of widely used k nearest neighbors, is presented. Within this framework, the SINs of test image are training images associated with the nonzero entries in the sparse representation of test image, and they...
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