Search

In this paper, we propose a robust block classification based semi-blind video watermarking algorithm using visual cryptography and SURF (Speed-Up Robust Features) features to enhance the robustness, stability, imperceptibility and real-time performance. A method of selecting the best frames in each shot and the best regions or blocks within best frames is proposed to avoid employing frame-by-frame method for generating owner's share in order to enhance robustness as well as reducing time complexity. In our method, Owner's share is generated using the classification of selected robust blocks within the chosen frames along with corresponding watermark information. In extraction process, the SURF features are employed to match the feature points of selected frames with all frames to detect selected frames. Moreover, we resynchronize the embedded regions from distorted video to original sequence using SURF feature points matching. Afterwards, based on these matched feature points, rotation and scaling parameters are estimated next, selected blocks are retrieved using side information being stored eventually, watermark information is reconstructed successfully. Selecting Best frames, best regions, and employing surf features make our method to be highly robust against various kinds of attacks including image processing attacks, geometrical attacks and temporal attacks. Experimental results confirm the superiority of our scheme in case of being applicable in the real world, enhancing robustness and exploiting idea imperceptibility, over previous related methods. [ABSTRACT FROM AUTHOR]

In various application domains, high-dimensional multi-label data has become more prevalent, presenting two significant challenges: instances with high-dimensional features and a large number of labels. In the context of multi-label feature selection, the objective is to choose a subset of features from a given set that is highly pertinent for predicting multiple labels or categories associated with each instance. However, certain characteristics of multi-label classification, such as label dependencies and imbalanced label distribution, have often been overlooked although they hold valuable insights for designing effective multi-label feature selection algorithms. In this paper, we propose a feature selection model which exploits explicit global and local label correlations to select discriminative features across multiple labels. In addition, by representing the feature matrix and label matrix in a shared latent space, the model aims to capture the underlying correlations between features and labels. The shared representation can reveal common patterns or relationships that exist across multiple labels and features. An objective function involving L 2 , 1 -norm regularization is formulated, and an alternating optimization-based iterative algorithm is designed to obtain the sparse coefficients for multi-label feature selection. The proposed method was evaluated on 14 real-world multi-label datasets using six evaluation metrics, through comprehensive experiments. The results indicate its effectiveness, surpassing that of several representative methods. [ABSTRACT FROM AUTHOR]