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Due to the ill-posed nature of image restoration, seeking a meaningful image prior is still a great challenge in the field of image processing. The total variation with overlapping group sparsity (OGS-TV) has been successfully applied for image denoising/deblurring. In this paper, we further study the overlapping group sparsity of the image gradient. The sparsity is measured by the ℓ q quasi-norm (0 < q < 1). The proposed regularizer comes down to the well-known hyper-Laplacian prior if the overlapping group size is 1. Although it seems to be a simple extensive study compared with the previous works, its regularization capability and corresponding mathematical problems are still in demand for imaging science. To solve the non-convex and non-smooth minimization problem, we use the alternating direction method of multipliers as the main algorithm framework. The difficult inner subproblem is tackled by the majorization-minimization method with the sophisticatedly derived majorizer. We carry out some numerical experiments to demonstrate the effectiveness of the proposed regularizer in terms of PSNR and SSIM values. [ABSTRACT FROM AUTHOR]

Dictionary learning has played an important role in the success of sparse representation. Although several dictionary learning approaches have been developed for image classification, discriminative dictionary pair learning, i.e., jointly learning a synthesis dictionary and an analysis dictionary, is still in its infant stage. In this paper, we proposed a novel model of Fisher discrimination dictionary pair learning (FDDPL), in which Fisher discrimination information is embedded into analysis representation, analysis dictionary, and synthesis dictionary representation. With the proposed Fisher-like discrimination term, discrimination of both synthesis dictionary representation and analysis dictionary representation is introduced into the dictionary pair learning model. An iterative algorithm to efficiently solve the proposed FDDPL and a FDDPL based classifier are also presented in this paper. The experiments on face recognition, scene categorization, gender classification, and action recognition clearly show the advantage of the proposed FDDPL. [ABSTRACT FROM AUTHOR]

In this paper, we propose two algorithms of the Nonnegative Tucker decomposition that contain manifold regularization terms used for capturing the underlying geometric structures, which are applied in clustering of image databases. In general, based on Tucker model in the form of least square estimation, that is to find interpretable basis factors, and achieve the compressed of original data. It usually supposes the structures of the processing databases are global linearity. However, in practical applications, such as the processing arguments of image, video sequence and Electroencephalography data, they are usually within richness structures of nonlinear. Therefore, it is necessary to uncover the underlying structures for achieving an effective application. Hence, in this article, we incorporate Laplacian Eigenmaps i.e., LE and Locally Linear Embedding i.e., LLE as the manifold regularization terms into the least square form of Tucker model. In addition, for the physical meaning of reasonable interpretation consistent with original data, thus, the basis factors and core tensor are constrained to be nonnegative. Finally, the numerical results have demonstrated both two our proposed algorithms can achieve an effective performance among state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • The images are fused simply based on a large and small-scale focus maps. • A better focus map can be estimated using the notion of random walks. • The above algorithm can be boosted by considering an equivalent objective function. • The fusion is computationally efficient yet robust to mis-registration and noise. Abstract In conventional fusion methods for multi-focus images, the focus map generated by a focus measure would usually be sensitive to mis-registration and noise, or produce badly-aligned boundaries. While many state-of-the-art algorithms use more complex strategies or procedures to address this problem, in this paper we propose to estimate a focus map directly from the two-scale imperfect observations (focus maps) obtained using a small and large-scale focus measures. This would contribute to a more robust fusion by taking advantage of the complementary properties of the two-scale observed focus maps, i.e., robustness to mis-registration (and noise) and the better aligned boundaries. The estimation is firstly modeled in a probabilistic perspective using random walks-based algorithm, in which we try to solve the probabilities that each pixel of the focus map is associated with the observed ones. Then we found that this method is equivalent to solving an alternate objective function, enabling a great boost both in computational efficiency and estimation result. Experimental results demonstrate that the proposed method is robust yet efficient compared with state-of-the-art fusion methods. [ABSTRACT FROM AUTHOR]

Abstract The purpose of image fusion is to integrate useful information from multiple images and produce a more reliable image. The key problem of multi-focus image fusion is how to determine the focused regions of the source images. As an effective and excellent fusion algorithm, the focused regions in the source images should be preserved as much as possible into the fused image. To accomplish this goal, a novel multi-focus image fusion method based on a focused regions boundary finding and multi-scale transform (MST) is proposed in this paper. The Meanshift algorithm is used to determine the focused regions first. Then, an edge detection method and morphological method are used to find the boundaries of the focused regions in the source images. For the focused boundary regions, the combination of pulse coupled neural network (PCNN) and Gaussian fuzzy method is used to produce the fused boundary region in nonsubsampled contourlet transform (NSCT) domain. Finally, the fused boundary region and the focused region of the source images are fused directly. The experimental results demonstrate that the proposed algorithm can accurately determine the focused regions, and at the same time, a better fused boundary region can be obtained; this algorithm is superior to conventional methods with respect to both objective quality evaluations and visual inspection. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance. [Copyright &y& Elsevier]

Abstract: Similarity measure plays an important role in many image processing fields. This paper introduces the medium mathematic systems, and establishes a novel image medium similarity measure between two pixels and that of between two image sets based on the measure of medium truth degree. Moreover, an image edge detection algorithm, an image matching algorithm and an image medium fidelity measure method governed by the medium similarity measure are discussed in this paper. Experimental results show that the proposed similarity measure is effective. [Copyright &y& Elsevier]

Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis. Although there exist several algorithms for SNMF in literature, their convergence and initialization have not been well addressed. In this paper, we first discuss the convergence and initialization of existing algorithms for SNMF. We then propose a Converged Algorithm for SNMF (called CASNMF) which minimizes the Euclidean distance between a symmetrical matrix and its approximation of SNMF. Based on the optimization principle and the local auxiliary function method, we prove that our presented CASNMF does not only converge to a stationary point, but also could be applied to the wider range of SNMF problems. In addition, CASNMF does not require that the initial values are nonzero. To verify our theoretical results, experiments on three data sets are conducted by comparing our proposed CASNMF with other existing methods. [ABSTRACT FROM AUTHOR]

This paper implements a real-time and directional counting algorithm using the Graphic Processing Unit (GPU) Programming for the purpose of detecting and counting people. We use the Compute Unified Device Architecture (CUDA) as the environment of the GPU programming. The proposed algorithm is implemented for detecting and counting people employing the single virtual line and two virtual lines, respectively, using three video streams and two GPU graphic cards GeForce GT 630 and GeForce GTX 550Ti. We first test the video streams on the algorithm by using GeForce GT 630 together with applying the single virtual line and two virtual lines, respectively. Then, we repeat the same procedures for the GPU graphic card GeForce GTX 550Ti. The obtained experimental results show that our proposed algorithm running on GPU can be successfully programmed and implemented for people detecting and counting problems. [ABSTRACT FROM AUTHOR]

In this paper, a multilayer graph-based saliency detection algorithm by merging fixation is proposed to effectively detect salient objects in complex scenes. First, the fixation location of an image is acquired by using fixation prediction models. This is based on a motivation that human visual attention system would quickly focus on salient regions before further processing. Then, by merging the fixation saliency map into an over-segmented region map, we can obtain a coarse detection result which most likely contains salient objects. To further improve the performance of our saliency detection, the next key idea is to leverage color contrast between superpixels as features in CIE-Lab space and resolve saliency estimation of coarse regions via a multilayer graph-based framework. The final saliency detection is achieved by combining the coarse detection result with multilayer saliency maps. Extensive experiments are conducted on five benchmark datasets. Experimental results show that the proposed method yields comparable or better results in terms of PR curve, ROC curve, and F-measure, and is robust to deal with both cluttered and clean scenes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel saliency detection algorithm. The saliency of an image element is defined not only as its contrast to the background but as its similarity to the foreground. First, we extract background seeds as well as their spatial layout information from image boundaries to compute the background-based saliency map. Second, we generate a compact foreground region from the first-stage saliency map to describe the appearance and location of the salient object and calculate the foreground-based saliency map accordingly. We integrate these two saliency maps and further refine the unified one to obtain a more smooth and accurate saliency map. Each component of the presented algorithm is evaluated on the public available datasets and the experimental results also show that the presented algorithm achieves favorable performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the scenario of big data, incorporating multiple attribute descriptions of the same subject (i.e., so-called multi-view data analysis) and the developing effective data visualization techniques have been one of the most important topics in the computer vision and machine learning community. In this paper, a multiple view source attributes embedding framework is proposed that can assemble various object attributes together and embed them into a low dimensional space to realize visual data structure demonstration. Classical sole attribute data embedding approaches such as MDS and SHE can be utilized under this framework. The core idea of this framework is to achieve a uniform contribution merging, which means every source attribute will contribute equally to the final embedded output. The underlying reason for this idea is that now that an attribute is input as a source and no prior weight is given beforehand, it should be treated equally. The most exciting advantage of this strategy is that it can avoid prejudice caused by major attributes (exerting due to their evident quantity advantage), which may lead the minor attributes be ignored. In addition, we propose a simple iterating algorithm to implement this framework, and plenty of experiments are conducted under diverse source attribute configurations to validate its effectiveness. [ABSTRACT FROM AUTHOR]

Learning-based partial differential equations (PDEs), which combine fundamental differential invariants into a nonlinear regressor, have been successfully applied to several computer vision and image processing problems. However, the gradient descent method (GDM) for solving the linear combination coefficients among differential invariants is time-consuming. Moreover, when the regularization or constraints on the coefficients become more complex, it is troublesome or even impossible to deduce the gradients. In this paper, we propose a new algorithm, called fast alternating time-splitting approach (FATSA), to solve the linear combination coefficients. By minimizing the difference between the expected output and the actual output of PDEs at each time step , FATSA can solve the linear combination coefficients much faster than GDM. More complex regularization or constraints can also be easily incorporated. Extensive experiments demonstrate that our proposed FATSA outperform GDM in both speed and quality. [ABSTRACT FROM AUTHOR]

The image parsing process gives labels to image regions, as well as information including shape, semantics and context. Although it is one of the most important features in the human visual system, automatic image parsing using computer vision techniques remains difficult due to computational issue. In this paper we introduce a novel method to address this limitation. Our system models an image as a set of regions and uses a novel hypotheses generation algorithm to get possible image parsing solutions for final re-scoring. Our proposed hypotheses generation algorithm, called Loopy Dynamic Programming (LDP), handles large search space efficiently and gives good parsing hypotheses for testing. With such capacity, we are able to apply more precise and complex image models to achieve better performance. In addition, our system can perform image segmentation, detection and classification simultaneously. Experimental results using Pascal VOC 2007 dataset show that the proposed technique achieves very promising performance in all the three tasks. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel region-based framework for multifocus image fusion. The core idea is to segment the in-focus regions from the input images and merge them up to produce an all-in-focus image. To this end, we propose three intuitive constraints on the fusion process and model them into three energy terms, i.e., reconstruction error, out-of-focus energy and smoothness regularization. The three terms are then formulated into an optimization framework problem to solve a segmentation map. We also propose a greedy algorithm to minimize the objective function, which alternatively updates each pixel in the segmentation map using a coarse-to-fine strategy. The fused image is finally generated by combining the segmented in-focus regions in each source image via the segmentation map. Our approach can yield a seamless result with much fewer ringing artifacts. Comparative experiments based on various synthesized and real images demonstrate that our approach outperforms the state-of-the-art methods. [Copyright &y& Elsevier]