4 results on '"Akhlaghian Tab, Fardin"'
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2. A new robust video watermarking algorithm based on SURF features and block classification.
- Author
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Bahrami, Zhila and Akhlaghian Tab, Fardin
- Subjects
ROBUST control ,VIDEO watermarking ,IMAGE reconstruction algorithms ,IMAGE retrieval ,FEATURE extraction ,FEATURE selection - Abstract
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]
- Published
- 2018
- Full Text
- View/download PDF
3. Multi-label feature selection with global and local label correlation.
- Author
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Faraji, Mohammad, Seyedi, Seyed Amjad, Akhlaghian Tab, Fardin, and Mahmoodi, Reza
- Subjects
- *
FEATURE selection , *MATRIX decomposition , *NONNEGATIVE matrices , *SHARED workspaces - Abstract
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]
- Published
- 2024
- Full Text
- View/download PDF
4. Unsupervised feature selection using orthogonal encoder-decoder factorization.
- Author
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Mozafari, Maryam, Seyedi, Seyed Amjad, Pir Mohammadiani, Rojiar, and Akhlaghian Tab, Fardin
- Subjects
- *
FEATURE selection , *FACTORIZATION , *MACHINE learning , *MATRIX decomposition , *NONNEGATIVE matrices - Abstract
Unsupervised feature selection (UFS) is a fundamental task in machine learning and data analysis, aimed at identifying a subset of non-redundant and relevant features from a high-dimensional dataset. Embedded methods seamlessly integrate feature selection into model training, resulting in more efficient and interpretable models. Current embedded UFS methods primarily rely on self-representation or pseudo-supervised feature selection approaches to address redundancy and irrelevant feature issues, respectively. Nevertheless, there is currently a lack of research showcasing the fusion of these two approaches. This paper proposes the Orthogonal Encoder-Decoder factorization for unsupervised Feature Selection (OEDFS) model, combining the strengths of self-representation and pseudo-supervised approaches. This method draws inspiration from the self-representation properties of autoencoder architectures and leverages encoder and decoder factorizations to simulate a pseudo-supervised feature selection approach. To further enhance the part-based characteristics of factorization, orthogonality constraints and local structure preservation restrictions are incorporated into the objective function. The optimization process is based on the multiplicative update rule, ensuring efficient convergence. To assess the effectiveness of the proposed method, comprehensive experiments are conducted on 14 datasets and compare the results with eight state-of-the-art methods. The experimental results demonstrate the superior performance of the proposed approach in terms of UFS efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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