A stereo video coding scheme which is compatible with monoview-processor is presented in this paper. At the same time, this paper proposes an adaptive prediction structure which can make different prediction modes to be applied to different groups of picture (GOPs) according to temporal correlations and interview correlations to improve the coding efficiency. Moreover, the most advanced video coding standard H.264 is used conveniently for maximize the coding efficiency in this paper. Finally, the effectiveness of the proposed scheme is verified by extensive experimental results.

Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.

A novel automatic image annotation (AIA) scheme is proposed based on multiple-instance learning (MIL). For a given concept, manifold ranking (MR) is first employed to MIL (referred as MR-MIL) for effectively mining the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images). With the mined positive instances, the semantic model of the concept is built by the probabilistic output of SVM classifier. The experimental results reveal that high annotation accuracy can be achieved at region-level.

The letter proposes a novel subsampling-based digital image watermarking scheme resisting the permutation attack. The subsampling-based watermarking schemes have drawn great attention for their convenience and effectiveness in recent years, but the traditional subsampling-based watermarking schemes are very vulnerable to the permutation attack. In this letter, the watermark information is embedded in the average values of the 1-level DWT coefficients to resist the permutation attack. The concrete embedding process is achieved by the quantization-based method. Experimental results show that the proposed scheme can resist not only the permutation attack but also some common image processing attacks.

This letter presents an improved visible watermarking scheme for halftone images. It incorporates watermark embedding into ordered dither halftoning by threshold modulation. The input images include a continuous-tone host image (e.g. an 8-bit gray level image) and a binary watermark image, and the output is a halftone image with a visible watermark. Our method is content adaptive because it takes local intensity information of the host image into account. Experimental results demonstrate effectiveness of the proposed technique. It can be used in practical applications for halftone images, such as commercial advertisement, content annotation, copyright announcement, etc.

A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.

The classified side-match vector quantizer, CSMVQ, has already been presented for low-bit-rate image encoding. It exploits a block classifier to decide which class the input vector belongs to using the variances of the upper and left codewords. However, this block classifier doesn't take the variance of the current input vector itself into account. This letter presents a new CSMVQ in which a two-level block classifier is used to classify input vectors and two different master codebooks are used for generating the state codebook according to the variance of the input vector. Experimental results prove the effectiveness of the proposed CSMVQ.

Self embedding watermarking is a technique used for tamper detection, localization and recovery. This letter proposes a novel self embedding scheme, in which the halftone version of the host image is exploited as a watermark, instead of a JPEG-compressed version used in most existing methods. Our scheme employs a pixel-wise permuted and embedded mechanism and thus overcomes some common drawbacks of the previous methods. Experimental results demonstrate our technique is effective and practical.

New methods for digital image watermarking based on the characteristics of vector quantization (VQ) are proposed. In contrast with conventional watermark embedding algorithms to embed only one watermark at a time into the original source, we present one algorithm to embed multiple watermarks for copyright protection. The embedding and extraction processes are efficient for implementing with conventional VQ techniques, and they can be accomplished in parallel to shorten the processing time. After embedding, embedder would output one watermarked reconstruction image and several secret keys associated with the embedded watermarks. These secret keys are then registered to the third party to preserve the ownership of the original source in order to prevent the attackers from inserting counterfeit watermarks. Simulation results show that under no attacks, the embedded watermarks could be perfectly extracted. If there are some intentional attacks in our simulation, all the watermarks could survive to protect the copyrights. Therefore, we are able to claim the robustness, usefulness, and ease of implementation of our algorithm.

This paper presents a novel digital image watermarking algorithm based on the labeled bisecting clustering technique. Each cluster is labeled either '0' or '1' based on the labeling key. Each input image block is then assigned to the nearest codeword or cluster centre whose label is equal to the watermark bit. The watermark extraction can be performed blindly. The proposed method is robust to JPEG compression and some spatial-domain processing operations. Simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.

This letter presents a no-reference blocking artifact measure based on analysis of color discontinuities in YUV color space. Color shift and color disappearance are first analyzed in JPEG images. For color-shifting and color-disappearing areas, the blocking artifact scores are obtained by computing the gradient differences across the block boundaries in U component and Y component, respectively. An overall quality score is then produced as the average of the local ones. Extensive simulations and comparisons demonstrate the efficiency of the proposed method.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

Sharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.

A new scheme for watermarking based on vector quantization (VQ) over a binary symmetric channel is proposed. By optimizing VQ indices with genetic algorithm, simulation results not only demonstrate effective transmission of watermarked image, but also reveal the robustness of the extracted watermark.

A novel watermarked MDC system based on the SFQ algorithm and the sub-sampling method is proposed in this paper. Sub-sampling algorithm is applied onto the transformed image to introduce some redundancy between different channels. Secret information is embedded into the preprocessed sub-images. Good performance of the new system to defense the noise and the compression attacks is shown in the experimental results.

This letter proposes a visible watermarking scheme for halftone images. It exploits HVS filtering to transform the image in binary domain into continuous-tone domain for watermark embedding. Then a codeword search operation converts the watermarked continuous-tone image into binary domain. The scheme is flexible for two weighting factors are involved to adjust the watermark embedding strength and the average intensity of the watermarked image. Moreover, it can be used in some applications where original continuous-tone images are not available and the halftoning method is unknown.

This letter presents a novel robust video watermarking scheme based on space-time interest points. These points correspond to inherent structures of the video so that they can be used as synchronization signals for watermark embedding and extraction. In the proposed scheme, local regions are generated using the space-time interest points, and the watermark is embedded into all the regions by quantization. It is a blind scheme and the watermark can be extracted from any position of the video. Experimental results show that the watermark is invisible and it can robustly survive traditional signal processing attacks and video-oriented attacks.

A new image watermarking scheme is presented to achieve high capacity information hiding and geometric invariance simultaneously. Visually salient region is introduced into watermark synchronization. The saliency value of a region is used as the quantitative measure of robustness, based on which the idea of locally most salient region (LMSR) is proposed to generate the disjoint invariant regions. A meaningful binary watermark is then encoded using Chinese Remainder Theorem (CRT) in transform domain. Simulation results and comparisons demonstrate the effectiveness of the proposed scheme.

The letter describes a phase perturbation attack to the Discrete Fourier Transform (DFT) and Phase Shift Keying (PSK) based watermarking scheme which is proposed in [3]. In that paper the watermark information is embedded in the phase of the DFT coefficients. But this kind of PSK based watermarking scheme is very vulnerable to the phase perturbation attack, when some noise is added on the phase of the DFT coefficients, the watermark can't be correctly extracted anymore, while the quality degradation of the attacked watermarked image is visually acceptable.