With the explosive growth of video data, learning compact discriminative binary representations for efficient video retrieval has become essential. Most existing self-supervised video hashing methods struggle to achieve consistent modeling across short-term and long-range temporal dependencies, resulting in hash codes that are less discriminative and more redundant. To address this, we propose a novel self-supervised video hashing method called CSL-Hash, based on a bidirectional Mamba encoder-decoder network. The encoder integrates residual convolution, channel attention, and bidirectional Mamba to achieve more comprehensive feature modeling, while the decoder employs bidirectional Mamba to preserve temporal semantics. Additionally, we design a new loss function consisting of reconstruction loss, contrastive loss, and orthogonality constraint loss to enhance the independence and discriminability of hash codes. Experimental results validate the effectiveness of CSL-Hash on multiple video retrieval datasets.

Video hashing is an effective method to solve copy detection problems. This paper proposes a video hashing with robust secondary frames (RSFs) and local tangent space alignment (LTSA) for copy detection. In the proposed scheme, the input video is uniformly grouped, and RSFs are calculated based on the video frame groups. Then, a tensor is obtained by stacking all RSFs, and the tensor is divided into multiple smaller tensors. The mean calculation is used to construct a matrix from these small tensors, and the low-dimensional features are extracted through LTSA. Moreover, the MobileNetV2 is used to calculate two feature maps from each RSF. The two feature maps are stacked to form a dual-channel feature map, which is then divided into multiple feature map blocks. The mean calculation is used to extract deep features from these blocks. Finally, the low-dimensional and deep features are binarized and concatenated to obtain a hash. Comparative experimental results show that our hashing scheme has the best copy detection and classification performance.

Dividing a Screen Content Image (SCI) with complex components into pictorial and textual regions for predicting scores is one of the common Screen Content Image Quality Assessment (SCIQA) methods. However, how to efficiently leverage pictorial and textual features to predict quality scores for no-reference SCIQA still needs to be explored. In addition, statistical analysis reveals that labels of SCIs present a distribution. Therefore, both the distribution of quality scores of SCIQA and the distribution of labels need to be considered in the SCIQA. This paper proposes a no-reference SCIQA method unifying pictorial and textual features. One contribution is the proposed dual-branch extraction module with the parameter-free attention convolution block and the joint prediction module. The proposed method employs the dual-branch extraction module to generate efficient pictorial and textual features and then uses the joint prediction module to predict quality scores. Another contribution is the joint distribution loss. It makes the distribution of the quality scores as close as possible to the distribution of labels. Experiments on the SCIQA datasets show that the proposed method achieves excellent SCIQA performance and generalization ability.

Copy detection is a key task of video copyright protection. This paper presents a robust video hashing with non-negative tensor factorization (NTF) for copy detection. In the presented video hashing scheme, secondary frames are computed from the preprocessed video by assigning weights to all frames within a video group based on color entropy. Next, the secondary frames are fed into the pre-trained MobileNetV2 and then NTF is exploited to compress the three-order tensor constructed by stacking the output feature maps for hash construction. Experiments conducted on publicly available video datasets indicate that the presented hashing scheme outperforms the evaluated hashing schemes in the performances of classification and copy detection.

Multi-view clustering has gained great progress recently, which employs the representations from different views for improving the final performance. In this paper, we focus on the problem of multi-view clustering based on the Markov chain by considering low-rank constraints. Since most existing methods fail to simultaneously characterize the relations among different entries in a tensor from the global perspective and describe local structures of similarity matrices of a tensor, we propose a novel Flexible Tensor Learning for Multi-view Clustering with the Markov chain (FTLMCM) to solve this problem. We also construct transition probability matrices based on the Markov chain to fully utilize the connection between the Markov chain and spectral clustering. Specifically, the low-rank constraints of the tensor, the frontal slices and the lateral slices of the tensor are imposed on the objective function of the proposed method to achieve these goals. Besides, these three constraints can be optimized jointly to achieve mutual refinement. FTLMCM also uses the tensor rotation to better explore the relationships among different views. We formulate FTLMCM as a problem of low-rank tensor recovery and solve it with the augmented Lagrangian multiplier. Experiments on six different benchmark data sets under six metrics demonstrate that the proposed method is able to achieve better clustering performance.

Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web. And it is challenging to restore embedded image from corrupted stego-image. This paper studies akNN-bit approximation algorithm to remove noises in embedded image. The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits. Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels. And then, if an unreliable bit locates at any one of the$5^{th}\sim 7^{th}$bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors. Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel. We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images.

This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of our TD hashing. A popular TD algorithm, called Tucker decomposition, is then exploited to decompose the three-order tensor into a core tensor and three orthogonal factor matrices. As the factor matrices can reflect intrinsic structure of original tensor, hash construction with the factor matrices makes a desirable discrimination of the TD hashing. To examine these claims, there are 14,551 images selected for our experiments. A receiver operating characteristics (ROC) graph is used to conduct theoretical analysis and the ROC comparisons illustrate that the TD hashing outperforms some state-of-the-art algorithms in classification performance between the robustness and discrimination.

This paper designs an efficient image hashing with a ring partition and a nonnegative matrix factorization (NMF), which has both the rotation robustness and good discriminative capability. The key contribution is a novel construction of rotation-invariant secondary image, which is used for the first time in image hashing and helps to make image hash resistant to rotation. In addition, NMF coefficients are approximately linearly changed by content-preserving manipulations, so as to measure hash similarity with correlation coefficient. We conduct experiments for illustrating the efficiency with 346 images. Our experiments show that the proposed hashing is robust against content-preserving operations, such as image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment, and image scaling. Receiver operating characteristics (ROC) curve comparisons are also conducted with the state-of-the-art algorithms, and demonstrate that the proposed hashing is much better than all these algorithms in classification performances with respect to robustness and discrimination.