Estimating Watermarking Capacity in Gray Scale Images Based on Image Complexity

Capacity is one of the most important parameters in image watermarking. Different works have been done on this subject with different assumptions on image and communication channel. However, there is not a global agreement to estimate watermarking capacity. In this paper, we suggest a method to find the capacity of images based on their complexities. We propose a new method to estimate image complexity based on the concept of Region Of Interest (ROI). Our experiments on 2000 images showed that the proposed measure has the best adoption with watermarking capacity in comparison with other complexity measures. In addition, we propose a new method to calculate capacity using proposed image complexity measure. Our proposed capacity estimation method shows better robustness and image quality in comparison with recent works in this field

医用画像処理-圧縮手法およびファジー論理を用いた診断支援解析手法-

制度:新 ; 文部省報告番号:甲794号 ; 学位の種類:工学博士 ; 授与年月日:1989-03-09 ; 早大学位記番号:新1515 ; 理工学図書館請求番号:1289早稲田大

Automatic image annotation by a loosely joint non‐negative matrix factorisation

Nowadays, the number of digital images has increased so that the management of this volume of data needs an efficient system for browsing, categorising and searching. Automatic image annotation is designed for assigning tags to images for more accurate retrieval. Non‐negative matrix factorisation (NMF) is a traditional machine learning technique for decomposing a matrix into a set of basis and coefficients under the non‐negative constraints. In this study, the authors propose a two‐step algorithm for designing an automatic image annotation system that employs the NMF framework for its first step and a variant of K‐nearest neighbourhood as its second step. In the first step, a new multimodal NMF algorithm is proposed to extract the latent factors which reflect the content of images. This is done by jointly factorising the visual and textual data feature matrices so that they have close representation, although not necessarily the same. In the second step, after mapping images to the latent factors space a few tags are predicted for the new images based on a weighted average of similar data. They evaluated the performance of the proposed method and compared it to the state‐of‐the‐art literature. Comparison results demonstrate the effectiveness and potential of the proposed method in image annotation applications