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The critical challenge of single image inpainting stems from accurate semantic inference via limited information while maintaining image quality. Typical methods for semantic image inpainting train an encoder-decoder network by learning a one-to-one mapping from the corrupted image to the inpainted version. While such methods perform well on images with small corrupted regions, it is challenging for these methods to deal with images with large corrupted area due to two potential limitations. 1) Such one-to-one mapping paradigm tends to overfit each single training pair of images; 2) The inter-image prior knowledge about the general distribution patterns of visual semantics, which can be transferred across images sharing similar semantics, is not explicitly exploited. In this paper, we propose the Generative Memory-guided Semantic Reasoning Model (GM-SRM), which infers the content of corrupted regions based on not only the known regions of the corrupted image, but also the learned inter-image reasoning priors characterizing the generalizable semantic distribution patterns between similar images. In particular, the proposed GM-SRM first pre-learns a generative memory from the whole training data to explicitly learn the distribution of different semantic patterns. Then the learned memory are leveraged to retrieve the matching semantics for the current corrupted image to perform semantic reasoning during image inpainting. While the encoder-decoder network is used for guaranteeing the pixel-level content consistency, our generative priors are favorable for performing high-level semantic reasoning, which is particularly effective for inferring semantic content for large corrupted area. Extensive experiments on Paris Street View, CelebA-HQ, and Places2 benchmarks demonstrate that our GM-SRM outperforms the state-of-the-art methods for image inpainting in terms of both visual quality and quantitative metrics. [ABSTRACT FROM AUTHOR]

Image denoising methods using deep neural networks have achieved a great progress in the image restoration. However, the recovered images restored by these deep denoising methods usually suffer from severe over-smoothness, artifacts, and detail loss. To improve the quality of restored images, we first propose Supplemental Priors (SP) method to adaptively predict depth-directed and sample-directed prior information for the reconstruction (decoder) networks. Furthermore, the over-parameterized deep neural networks and too precise supplemental prior information may cause an over-fitting, restricting the performance promotion. To improve the generalization of denoising networks, we further propose Regularization Priors (RP) method to flexibly learn depth-directed and dataset-directed regularization noise for the retrieving (encoder) networks. By respectively integrating the encoder and decoder with these plug-and-play RP block and SP block, we propose the final Adaptive Prior Denoising Networks, called APD-Nets. APD-Nets is the first attempt to simultaneously regularize and supplement denoising networks from the adaptive priors’ view with drawing learning-based mechanism into producing adaptive regularization noise and supplemental information. Extensive experiment results demonstrate our method significantly improves the generalization of denoising networks and the quality of restored images with greatly outperforming the traditional deep denoising methods both quantitatively and visually. The code will be released at https://github.com/JiangBoCS/APD-Nets. [ABSTRACT FROM AUTHOR]

The informativeness and discriminativeness of features collaboratively ensure high-accuracy Facial Expression Recognition (FER) in the wild. Most of existing methods use the single-path deep convolutional neural network with softmax loss for basic FER, while they cannot deal with the challenging situations of the compound FER in the wild, because they fail to learn informative and discriminative features in a targeted manner. To this end, we present an Informative and Discriminative Feature Learning (IDFL) framework that consists of two key components: the Multi-Path Attention Convolutional Neural Network (MPACNN) and Balanced Separate loss (BS loss), for both basic and compound high-accuracy FER in the wild. Specifically, MPACNN leverages different paths to learn diverse features. These features are then adaptively fused into informative ones via an attention module, such that the model can adequately capture detailed information for both basic and compound FER. The BS loss maximizes the inter-class distance of features and minimizes the intra-class one. In this way, the features are discriminative enough for high-accuracy FER in the wild. Particularly, the BS loss is invoked as the objective function of MPACNN, so the model can learn informative and discriminative features at the same time, yielding better performance. Seven databases are utilized to evaluate the proposed method, and the results demonstrate that our method achieves state-of-the-art performance on both basic and compound expressions with good generalization ability. Moreover, our model contains fewer parameters and can be trained faster than other related models. [ABSTRACT FROM AUTHOR]

Facial Expression Recognition (FER) is a long-standing but challenging research problem in computer vision. Existing approaches mainly focus on single-label emotional prediction, which cannot handle the complex multi-label FER task because of the coupling behavior of multiple emotions on a single facial image. To this end, in this paper, we propose a novel Self-supervised Exclusive-Inclusive Interactive Learning (SEIIL) method to facilitate discriminative multi-label FER in the wild, which can effectively handle the coupled multiple sentiments with limited unconstrained training data. Specifically, we construct an emotion disentangling module to capture the inclusive and exclusive characteristics of facial expressions, which can decouple the compound numerous emotions on an image. Moreover, an adaptively-weighted ensemble technique is conceived to aggregate category-level latent exclusive embeddings, and then a conditional adversarial interactive learning module is designed to fully leverage the complementary between the inclusive and formulated latent representations. Furthermore, to tackle the insufficient data for training, we introduce a self-supervised learning strategy to augment the amount and diversity of facial images, which can endow the model with advanced generalization ability. Under this strategy, the proposed two modules can be concurrently utilized in our SEIIL to jointly handle the coupled emotions and alleviate the overfitting problem. Extensive experimental results on six databases illustrate the superb performance of our method against state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Automated multi-label chest X-ray (CXR) image classification has recently made significant progress in clinical diagnosis based on the advanced deep learning techniques. However, most existing methods mainly focus on analyzing locality visual cues from a single image but fail to leverage the underlying explicit correlations among different images for precise disease diagnosis. By contrast, an experienced radiologist expertizes in transferring knowledge from previous tasks to diagnose the present radiograph. To enable the machine like a radiologist, this paper proposes a novel Semantic Similarity Graph Embedding (SSGE) framework, which explicitly explores the semantic similarities among images to optimize the visual feature embedding for improving the performance of multi-label CXR images classification. Specifically, the proposed SSGE framework contains three main components: the image feature embedding (IFE) module, similarity graph construction (SGC) module, and semantic similarity learning (SSL) module. To realize interactive teaching and learning between visual and semantic information, the proposed SSGE framework is built on the “Teacher-Student” (semantic-visual) learning mechanism. With the guidance and supervision of the cross-image similarity graph generated by the SGC module, the SSL module leverages Graph Convolutional Network (GCN) to adaptively recalibrate the multi-image feature representations extracted from the IFE module, which guarantees their semantic consistency. Furthermore, we propose a novel re-weighting strategy to learn a more optimal semantic-similarity graph for the information propagation of the GCN layers. Extensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method in comparison with some state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

The performances of person re-identification tasks can be seriously degraded because of variations caused by view changes. In recent years, there are many methods focusing on how to solve cross view challenges which can be roughly divided into two categories: 1) learning view-invariant features without the help of view information. 2) combining view-wise features with the guide of view information. However, these methods are neither perfect enough. Methods of the first category are not roust enough for different kinds of view-invariants while methods of the other category can not generalize well in real-world applications. In this paper, we aim to learn view-invariant features with the help of view information. We proposed an end-to-end trainable framework, called View Confusion Feature Learning (VCFL), to learn view-invariant features by getting rid of view specific information. To the best of our knowledge, VCFL is originally proposed to learn view-invariant identity-wise features, and it is a kind of combination of view-generic and view-specific methods. The whole view confusion learning mechanism consists of three parts: 1) adversarial learning between feature extractor and the view classifier; 2) drawing the features with the same ID close to centers; 3) the guidance of SIFT, for seamlessly integration of hand-crafted features and deep features. In order to make the whole confusion mechanism work better, we further propose a VCFL+ model, which improves the fusion process in the feature map level through the thoughts of attention mechanism. Experiments on three benchmark datasets including Market1501, CUHK03, and DukeMTMC prove the superiority of our method over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Image brightness in color representation has caught active research interests, while its influence to texture features is relatively rarely studied. In this paper, we address the issue of illuminance, or brightness, interference to texture descriptors, especially to the Gabor filter based approaches. Firstly, we reveal the fact that the Gabor filter response linearly drifts with the illuminance. Secondly, the Hodge decomposition is introduced, and a linear regression model is presented to interpret the linear drift phenomenon. An interesting connection with the intrinsic image decomposition is established. Finally, a partial differential equation based method is proposed to compensate the drift issue. Consequently, the texture of the compensated image is enhanced. Extensive experiments are conducted to verify the proposed statements. The proposed linear regression model is verified empirically, and the compensation performances are demonstrated. Classification results on various databases indicate that the compensation can effectively improve the capacity of many existing texture description methods. Especially, the results show that the boosting effect on some deep learning based methods is also valid. [ABSTRACT FROM AUTHOR]

Recently, learning to hash has been widely studied for image retrieval thanks to the computation and storage efficiency of binary codes. Most existing learning to hash methods have yielded significant performance. However, for most existing learning to hash methods, sufficient training images are required and used to learn precise hashing codes. In some real-world applications, there are not always sufficient training images in the domain of interest. In addition, some existing supervised approaches need a amount of labeled data, which is an expensive process in terms of time, labor and human expertise. To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain. In GTH, we aim to learn domain-invariant hashing functions. To achieve that, we propose to minimize the error matrix between two hashing projections of target and source domains. We seek for the maximum likelihood estimation (MLE) solution of the error matrix between the two hashing projections due to the domain gap. Furthermore, an alternating optimization method is adopted to obtain the two projections of target and source domains. By doing so, two projections can be progressively aligned. Extensive experiments on various benchmark databases for cross-domain visual recognition verify that our method outperforms many state-of-the-art learning to hash methods. The source code is available at https://github.com/liuji93/GTH. [ABSTRACT FROM AUTHOR]

High-resolution fingerprint recognition has been a hot topic for many years. Compared with a traditional fingerprint image, a high-resolution fingerprint image can provide more features, such as pores and ridge contours. Introducing these features into fingerprint comparison and recognition can improve the recognition accuracy and reduce the risk of identification errors. This paper proposes a novel method for comparing pores on high-resolution fingerprint images. The method can be divided into two steps. In the first step, fingerprints are aligned using the pixel-category-distance-based data-driven descending algorithm. Traditionally, fingerprints are aligned based on feature points, such as minutiae and singular points. Such alignment methods are not suitable when dealing with partial fingerprints because small overlapping areas often do not contain enough features to guarantee a correct alignment. In this research, the ridges and valleys on fingerprints are used in combination with the orientation field for alignment. The proposed algorithm performs well when aligning both partial and full fingerprints. The common areas between the two images can be estimated based on the alignment result. In the second step, pores lying in the common areas are selected for comparison. To improve the comparison accuracy, pores are compared using local features and spatial relations. A graph comparison algorithm is designed in this step. The experimental results show that the proposed method is more accurate than other state-of-the-art pore comparison algorithms. [ABSTRACT FROM AUTHOR]

Preserving projection-based methods are good for finding the manifold structure embedded in data. As they use the Euclidean distance as a metric, which is sensitive to noise and outliers in data, nuclear norm-based 2D locality preserving projection (NN-2DLPP) is thus proposed to improve the robustness of 2DLPP. However, NN-2DLPP does not consider the discriminant ability of data. In order to improve the discriminant ability of preserving projection methods, in this paper, we use preserving projection learning with structurally incoherence of data and propose structurally incoherent low-rank 2DLPP (SILR-2DLPP) for image classification. This approach provides a discriminative representation of preserving projection learning by recovering the distinct different classes of the data. SILR-2DLPP searches the optimal subspace and low-rank representation simultaneously. We further extend SILR-2DLPP to a kernel case and propose kernel SILR-2DLPP (KSILR-2DLPP) to obtain a nonlinear representation. The theoretical analysis including the convergence and computational complexity of SILR-2DLPP are presented. To verify the performance of SILR-2DLPP and KSILR-2DLPP, six well-known image databases were used in the experiments. The experimental results show that the proposed methods are superior to the previous preserving projection methods for image classification. [ABSTRACT FROM AUTHOR]

2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF), known as a famous matrix factorization technique, has been widely used in pattern recognition and computer vision. NMF represents the input data matrix as a product of two nonnegative factors. As NMF is based on the Euclidean distance, which is sensitive to noise or errors in the data, some robust NMF methods are proposed. Mainly focusing on parts-based representation, these robust NMF methods often neglect global representation of data. In fact, the global geometry information of data is more robust than the local information about the noisy data in terms of image classification. In order to effectively improve the robustness of NMF and learn part-based and global representation of the data, a novel method low-rank nonnegative factorization (LRNF) is proposed in this paper. First, we assume that the data are grossly corrupted, and the $L_{1} $ norm is used as a sparse constraint on the assumed noise matrix. Then, LRNF learns a low-rank matrix with the global representation ability. Finally, we make a nonnegative factorization of the learned low-rank matrix. We can obtain a base matrix, which preserves locality and globality properties of the data in the meantime. Extensive experiments have been conducted on nine real-world image databases to verify the performance of the proposed LRNF method by comparing with the state-of-the-art algorithms on robust dimensionality reduction. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification. NDMF integrates the nonnegative constraint, orthogonality, and discriminant information in the objective function. NDMF considers the incoherent information of both factors in standard NMF and is proposed to enhance the discriminant ability of the learned base matrix. NDMF projects the low-dimensional representation of the subspace of the base matrix to regularize the NMF for discriminant subspace learning. Based on the Euclidean distance metric and the generalized Kullback–Leibler (KL) divergence, two kinds of iterative algorithms are presented to solve the optimization problem. The between- and within-class scatter matrices are divided into positive and negative parts for the update rules and the proofs of the convergence are also presented. Extensive experimental results demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art discriminant NMF algorithms. [ABSTRACT FROM PUBLISHER]

As an important biometric feature, human gait has great potential in video-surveillance-based applications. In this paper, we focus on the matrix representation-based human gait recognition and propose a novel discriminant subspace learning method called sparse bilinear discriminant analysis (SBDA). SBDA extends the recently proposed matrix-representation-based discriminant analysis methods to sparse cases. By introducing the \(L_{1} \) and \(L_{2} \) norms into the objective function of SBDA, two interrelated sparse discriminant subspaces can be obtained for gait feature extraction. Since the optimization problem has no closed-form solutions, an iterative method is designed to compute the optimal sparse subspace using the \(L_{1}\) and \(L_{2} \) norms sparse regression. Theoretical analyses reveal the close relationship between SBDA and previous matrix-representation-based discriminant analysis methods. Since each nonzero element in each subspace is selected from the most important variables/factors, SBDA is potential to perform equivalent to or even better than the state-of-the-art subspace learning methods in gait recognition. Moreover, using the strategy of SBDA plus linear discriminant analysis (LDA), we can further improve the performance. A set of experiments on the standard USF HumanID and CASIA gait databases demonstrate that the proposed SBDA and SBDA + LDA can obtain competitive performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel object tracking framework by joint registration and active contour segmentation (JRACS), which can robustly deal with the non-rigid shape changes of the target. The target region, which includes both foreground and background pixels, is implicitly represented by a level set. A Bhattacharyya similarity based metric is proposed to locate the region whose foreground and background distributions best match those of the tracked target. Based on this metric, a tracking framework that consists of a registration stage and a segmentation stage is then established. The registration step roughly locates the target object by modeling its motion as an affine transformation, and the segmentation step refines the registration result and computes the true contour of the target. The robust tracking performance of the proposed JRACS method is demonstrated by real video sequences where the objects have clear non-rigid shape changes. [ABSTRACT FROM PUBLISHER]

Subspace learning is an effective and widely used image feature extraction and classification technique. However, for the large-scale image recognition issue in real-world applications, many subspace learning methods often suffer from large computational burden. In order to reduce the computational time and improve the recognition performance of subspace learning technique under this situation, we introduce the idea of parallel computing which can reduce the time complexity by splitting the original task into several subtasks. We develop a parallel subspace learning framework. In this framework, we first divide the sample set into several subsets by designing two random data division strategies that are equal data division and unequal data division. These two strategies correspond to equal and unequal computational abilities of nodes under parallel computing environment. Next, we calculate projection vectors from each subset in parallel. The graph embedding technique is employed to provide a general formulation for parallel feature extraction. After combining the extracted features from all nodes, we present a unified criterion to select most distinctive features for classification. Under the developed framework, we separately propose supervised and unsupervised parallel subspace learning approaches, which are called parallel linear discriminant analysis (PLDA) and parallel locality preserving projection (PLPP). PLDA selects features with the largest Fisher scores by estimating the weighted and unweighted sample scatter, while PLPP selects features with the smallest Laplacian scores by constructing a whole affinity matrix. Theoretically, we analyze the time complexities of proposed approaches and provide the fundamental supports for applying random division strategies. In the experiments, we establish two real parallel computing environments and employ four public image and video databases as the test data. Experimental results demonstrate that the proposed approaches outperform several related supervised and unsupervised subspace learning methods, and significantly reduce the computational time. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a two-phase test sample representation method for face recognition. The first phase of the proposed method seeks to represent the test sample as a linear combination of all the training samples and exploits the representation ability of each training sample to determine M “nearest neighbors” for the test sample. The second phase represents the test sample as a linear combination of the determined M nearest neighbors and uses the representation result to perform classification. We propose this method with the following assumption: the test sample and its some neighbors are probably from the same class. Thus, we use the first phase to detect the training samples that are far from the test sample and assume that these samples have no effects on the ultimate classification decision. This is helpful to accurately classify the test sample. We will also show the probability explanation of the proposed method. A number of face recognition experiments show that our method performs very well. [ABSTRACT FROM AUTHOR]

Introduces image information-restoration algorithms based on long-range correlation. Comparison between image information-algorithms and other traditional techniques; Reduction of the blocking effect; Efficacy of the technique in eliminating random-valued impulse noises in images.