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This paper presents an online feature selection algorithm for video object tracking. Using the object and background pixels from the previous frame as training samples, we model the feature selection problem as finding a good subset of features to better classify object from background in current frame. This paper aims to improve existing methods by taking correlation between features into consideration. We propose to use AdaBoost algorithm to iteratively select one feature which best compensates the previously selected features. Using the selected features, we then construct a compound likelihood image, which shows the ability to discriminate better than the original frame, as the input for the tracking process. We also propose to use ellipse fitting to eliminate mislabeled pixels from our training process. In addition, we propose an online feature validity test to monitor the selected features and only re-select features when the previously selected features become out-of-date. Experimental results demonstrate that the proposed algorithm combined with mean-shift based tracking algorithm achieves very promising results. Index Terms--AdaBoost, feature selection, mean-shift, object tracking.

INTRODUCTION The effective protection of intellectual property rights is of priority concern in the light of the accelerated growth of digital media. Digital watermarking has been increasingly recognized as a […], Digital watermarking has been increasingly recognized as a highly effective means of protecting the intellectual property rights associated with multimedia data. Based on the multiresolution structures of wavelet decomposition, both on a real field and on binary field, we propose a multiresolution watermarking technique. In this work, we embed each decomposed layer of a binary watermark into the corresponding decomposed layer of a host image. Therefore, in case of attacks or progressive transmission, the coarser approximation of a watermark will be preserved in the coarser version of an image, but not be corrupted entirely. In the application of progressive transmission, adding higher frequency components, allows us to obtain higher resolution image, and, correspondingly, extract a higher resolution watermark. Experimental results demonstrate the robustness and validity of the watermarking process proposed herein.

The challenge of single-image dehazing mainly comes from double uncertainty of scene radiance and scene transmission. Most existing methods focus on restoring the visibility of hazy images and tend to derive a rough estimate of scene transmission. Unlike previous work, in this paper we advocate the significance of accurate transmission estimation and recast our problem as deriving the optimal transmission map directly from the haze model under two scene priors. We introduce theoretic and heuristic bounds of scene transmission to guide the optimum and show that the proposed theoretic bound happens to justify the well-known dark channel prior of haze-free images. With the constraints on the solution space, we then incorporate two scene priors, including locally consistent scene radiance and context-aware scene transmission, to formulate a constrained minimization problem and solve it by quadratic programming. The global optimality is guaranteed. Simulations on synthetic data set quantitatively verify the accuracy and show that the transmission map successfully captures fine-grained depth boundaries. Experimental results on color/gray-level images demonstrate that our method outperforms most state of the arts in terms of both accurate transmission maps and realistic haze-free images. [ABSTRACT FROM AUTHOR]

The success of research on matrix completion is evident in a variety of real-world applications. Tensor completion, which is a high-order extension of matrix completion, has also generated a great deal of research interest in recent years. Given a tensor with incomplete entries, existing methods use either factorization or completion schemes to recover the missing parts. However, as the number of missing entries increases, factorization schemes may overfit the model because of incorrectly predefined ranks, while completion schemes may fail to interpret the model factors. In this paper, we introduce a novel concept: complete the missing entries and simultaneously capture the underlying model structure. To this end, we propose a method called simultaneous tensor decomposition and completion (STDC) that combines a rank minimization technique with Tucker model decomposition. Moreover, as the model structure is implicitly included in the Tucker model, we use factor priors, which are usually known a priori in real-world tensor objects, to characterize the underlying joint-manifold drawn from the model factors. By exploiting this auxiliary information, our method leverages two classic schemes and accurately estimates the model factors and missing entries. We conducted experiments to empirically verify the convergence of our algorithm on synthetic data and evaluate its effectiveness on various kinds of real-world data. The results demonstrate the efficacy of the proposed method and its potential usage in tensor-based applications. It also outperforms state-of-the-art methods on multilinear model analysis and visual data completion tasks. [ABSTRACT FROM AUTHOR]

We propose a human motion extrapolation algorithm that synthesizes new motions of a human object in a still image from a given reference motion sequence. The algorithm is implemented in two major steps: contour manifold construction and object motion synthesis. Contour manifold construction searches for low-dimensional manifolds that represent the temporal-domain deformation of the reference motion sequence. Since the derived manifolds capture the motion information of the reference sequence, the representation is more robust to variations in shape and size. With this compact representation, we can easily modify and manipulate human motions through interpolation or extrapolation in the contour manifold space. In the object motion synthesis step, the proposed algorithm generates a sequence of new shapes of the input human object in the contour manifold space and then renders the textures of those shapes to synthesize a new motion sequence. We demonstrate the efficacy of the algorithm on different types of practical applications, namely, motion extrapolation and motion repair. [ABSTRACT FROM PUBLISHER]

Video inpainting is an important video enhancement technique used to facilitate the repair or editing of digital videos. It has been employed worldwide to transform cultural artifacts such as vintage videos/films into digital formats. However, the quality of such videos is usually very poor and often contain unstable luminance and damaged content. In this paper, we propose a video inpainting algorithm for repairing damaged content in digitized vintage films, focusing on maintaining good spatiotemporal continuity. The proposed algorithm utilizes two key techniques. Motion completion recovers missing motion information in damaged areas to maintain good temporal continuity. Frame completion repairs damaged frames to produce a visually pleasing video with good spatial continuity and stabilized luminance. We demonstrate the efficacy of the algorithm on different types of video clips. [ABSTRACT FROM PUBLISHER]

Due to the popularity of JPEG as an image compression standard, the ability to detect tampering in JPEG images has become increasingly important. Tampering of compressed images often involves recompression and tends to erase traces of tampering found in uncompressed images. In this paper, we present a new technique to discover traces caused by recompression. We assume all source images are in JPEG format and propose to formulate the periodic characteristics of JPEG images both in spatial and transform domains. Using theoretical analysis, we design a robust detection approach which is able to detect either block-aligned or misaligned recompression. Experimental results demonstrate the validity and effectiveness of the proposed approach, and also show it outperforms existing methods. [ABSTRACT FROM PUBLISHER]

Looks at the development of a multiresolution-based watermarking technique used to enclose digital watermarks into images, in image processing. In-depth look at the technique; Effectiveness of the technique; Decomposition of the multiresolution signal; Discussion on data distribution.

Presents information on a study which focused on a proposed image authentication technique by embedding digital watermarks into images. Description of the embedding methods; Information on the watermark extraction method; Experimental results; Issues regarding the proposed method; Conclusions.

Presents information on the proposed MPEG-based technique for embedding digital `watermarks' into digital video. Method used to hide secret information into the signals to discourage unauthorized copying or attest the origin of the media; Advantages of the proposed method; Discussion on the experimental results.

Proposes the application of wavelet transform as a tool for image mosaic. Application of wavelet transform; Combination of signals; Information content analysis of signals; Emergence of wavelet theory.

Describes a fast computation algorithm for the two-dimensional discrete cosine transform (2-D DCT). Capabilities of the algorithm; Complexity analysis of the post-addition stage.

Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

This paper presents a generalized Bayesian framework for relevance feedback in content-based image retrieval. The proposed feedback technique is based on the Bayesian learning method and incorporates a time-varying user model into the formulation. We define the user model with two terms: a target query and a user conception. The target query is aimed to learn the common features from relevant images so as to specify the user's ideal query. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore, at each feedback step, the learning process updates not only the target distribution, but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. We formulate the matching criterion using a weighting scheme and proposed a region clustering technique to determine the region correspondence between relevant images. With the proposed region clustering technique, we derive a representation in region level to characterize the target query. Experiments demonstrate that the proposed method combined with time-varying user model indeed achieves satisfactory results and our proposed region-based techniques further improve the retrieval accuracy.