Ce Li

An integrated visual saliency-based watermarking approach for synchronous image authentication and copyright protection

This paper proposes an integrated visual saliency-based watermarking approach, which can be used for both synchronous image authentication and copyright protection. Firstly, regions of interest (ROIs), which are not in a fixed size and can present the most important information of one image, would be extracted automatically using the proto-object based saliency attention model. Secondly, to resist common signal processing attacks, for each ROI, an improved quantization method is employed to embed the copyright information into its DCT coefficients. Finally, the edge map of one ROI is chosen as the fragile watermark, and is then embedded into the DWT domain of the watermarked image to further resist the tampering attacks. Using ROI-based visual saliency as a bridge, this proposed method can achieve image authentication and copyright protection synchronously, and it can also preserve much more robust information. Experimental results on standard benchmark demonstrate that compared with the state-of-the-art watermarking scheme, the proposed method is more robust to white noise, filtering and JPEG compression attacks. Furthermore, it also shows that the proposed method can effectively detect tamper and locate forgery.

Proto-Object Based Rate Control for JPEG2000: An Approach to Content-Based Scalability

The JPEG2000 system provides scalability with respect to quality, resolution and color component in the transfer of images. However, scalability with respect to semantic content is still lacking. We propose a biologically plausible salient region based bit allocation mechanism within the JPEG2000 codec for the purpose of augmenting scalability with respect to semantic content. First, an input image is segmented into several salient proto-objects (a region that possibly contains a semantically meaningful physical object) and background regions (a region that contains no object of interest) by modeling visual focus of attention on salient proto-objects. Then, a novel rate control scheme distributes a target bit rate to each individual region according to its saliency, and constructs quality layers of proto-objects for the purpose of more precise truncation comparable to original quality layers in the standard. Empirical results show that the suggested approach adds to the JPEG2000 system scalability with respect to content as well as the functionality of selectively encoding, decoding, and manipulation of each individual proto-object in the image, with only some slightly trivial modifications to the JPEG2000 standard. Furthermore, the proposed rate control approach efficiently reduces the computational complexity and memory usage, as well as maintains the high quality of the image to a level comparable to the conventional post-compression rate distortion (PCRD) optimum truncation algorithm for JPEG2000.

Key object-based static video summarization

In this paper, we present a system for object-based video summarization facilitated by an efficient video object segmentation system. We eliminate the redundancy not only from spatial and temporal domain, but also from content domain. First, we detect shot boundaries and extract video objects by a 3D graph-based algorithm. Once the objects are obtained, the shape of the objects need to be represented. The key objects are extracted in a global manner by K-means clustering of shapes. Experimental results on the proposed object-based scheme combined with efficient video object segmentation show desirable summarization.

Robust iterative closest point algorithm for registration of point sets with outliers

The problem of registering point sets with outliers including noises and missing data is discussed in this paper. To solve this problem, a novel objective function is proposed by introducing an overlapping percentage for partial registration. Moreover, a novel robust iterative closest point (ICP) algorithm is proposed which can compute rigid transformation, correspondence, and overlapping percentage automatically at each iterative step. This new algorithm uses as many point pairs as possible to yield a more reliable and accurate registration result between two m-D point sets with outliers. Experimental results demonstrate that our algorithm is more robust than the traditional ICP and the state-of-the-art algorithms.

Image splicing detection based on Markov features in QDCT domain

Abstract Image splicing is very common and fundamental in image tampering. Therefore, image splicing detection has attracted more and more attention recently in digital forensics. Gray images are used directly, or color images are converted to gray images before be processed in previous image splicing detection algorithms. However, most forgery images are color images. In order to make use of the color information in images, a classification algorithm is put forward which can use color images directly. In this paper, an algorithm based on Markov in quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection. First of all, color information is extracted from blocked images to construct quaternion in a whole manner, and the QDCT coefficients of quaternion blocked images can be obtained. Secondly, the expanded Markov features generated from the transition probability matrices in QDCT domain can not only capture the intra-block, but also the inter-block correlation between block QDCT coefficients. Finally, support vector machine (SVM) is exploited to classify the Markov feature vector. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can yield considerably better detection performance compared with the state-of-the-art splicing detection methods tested on the same dataset.

Spatio-temporal saliency perception via hypercomplex frequency spectral contrast.

Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

Authentication and copyright protection watermarking scheme for H.264 based on visual saliency and secret sharing

This paper proposes a video watermarking scheme based on visual saliency and secret sharing, which can be used for both synchronous authentication and copyright protection. Firstly, video is divided into several different scenes, and region of interest (ROI) for each I frames would be extracted automatically using the proto-object based saliency attention model. Secondly, the robust watermark and fragile watermark are generated according to ROI. The robust watermark is synthesized by copyright information and ROI of the first I frame for each video scene, and the edge map of ROI in each I frame is chosen as the fragile watermark. Finally, the robust watermark and fragile watermark are embedded into I frames of video scenes so that it can achieve video authentication and copyright information simultaneously. Experimental results demonstrate that the proposed method is robust to recompression and frame attack, and is also sensitive to tamper at the same time.

3D spatio-temporal graph cuts for video objects segmentation

In this paper, we present a method to extract moving objects in monocular image sequences. The proposed method is based on graph cuts defined on a spatio-temporal region adjacency graph (RAG). First, we initially over-segment each frame in the video, and take the over-segmented regions as the vertices in the 3D spatio-temporal graph. Second, multiple cues are fused together to extract objects accurately. Finally, accurate foreground/background segmentation are efficiently achieved by binary graph cut. The experimental results showed that the proposed method improved the performance of segmentation with respect to the popular methods.

Nonparametric bottom-up saliency detection using hypercomplex spectral contrast

Saliency detection is an useful technique for image semantic analysis such as auto image segmentation, image retargeting, advertising design and image compression. Inspired by two existing saliency detection algorithms, named spectral residual (SR) and phase spectrum of quaternion Fourier transform (PQFT), we propose a new bottom-up saliency detection method which is featured with the introduction of hypercomplex spectral contrast (HSC) in saliency detection. The proposed HSC algorithm introduces the HSV color image vector space in hypercomplex number, and is better comprehensive to consider amplitude spectral contrast into saliency model as well as phase spectral contrast. Meanwhile, we also incorporate the human vision nonuniform sampling into our model, which is a common phenomenon that directs visual attention to the logarithmic center of image in natural scenes. Experimental results on two public saliency detection datasets show that our approach performs better than four state-of-the art approaches remarkably.

Video object segmentation with shape cue based on spatiotemporal superpixel neighbourhood

In this study, the authors present a method to extract moving objects in image sequences. The proposed approach is based on a graph cuts algorithm defined on a spatiotemporal superpixel neighbourhood. Presegmented superpixels are partitioned into foreground and background while preserving temporal and spatial coherence. It achieves this goal by three steps. First, instead of operating at pixel level, the superpixels are advocated as basic units of the authors segmentation scheme. Second, within the graph cuts framework, two superpixel-based data terms and two superpixel-based smoothness terms are proposed to solve segmentation problem. Finally, the proposed method yields the segmentation of all the superpixels within video volume by the graph cuts algorithm. To illustrate the advantages of this approach, the quantitative and qualitative results are compared with other state-of-the-art methods. The experimental results show that the proposed method gives better performance of segmentation with respect to these methods.