Recently, the powerful attention mechanism has been wildly used to learn the fine-grained cross-modal correspondences. However, the trade-off between effectiveness and efficiency sometimes bothers existing attention-mechanism-based meth-ods. To address this deficiency, we propose a novel Triple-streamed architecture with a newly designed Memory fusion Transformer Encoder (Tri-MTE) for fine-grained cross-modal retrieval. Specifically, the whole model reserves the “late fusion” strategy thus ensuring efficiency. To strengthen the inter-modality interaction and improve the effectiveness, a memory fusion stream is designed and inserted between the modality streams to remember the modality-irrelevant infor-mation. Encoding such information to the modality represen-tation would significantly enhance the cross-modal retrieval performance. Finally, a bionic memory activation constrain-t is proposed to aid the learning procedure. Extensive ex-periments on two benchmark datasets show that the proposed method achieves promising results.

Various kinds of features prove to be effective for content-based image retrieval. However, due to the diversity of image contents, a descriptor may achieve impressive performance on specific images while becoming invalid on others. Although some efforts have been made to combine features as complementary counterparts, proper weighting scheme is still a challenge for fast and accurate retrieval. In this paper, we propose an effective fusion method, termed as Topo-correlation (Topo), where the importance of each feature is measured by cross-view correlations on local affinity graphs. Specifically, the weights of similarities are node-sensitive as well as modality-sensitive, thus boosting the results of good cues while depressing adverse factors for individual images. By estimating the consensus of similarity scores with regard to a query-driven criterion, the weighted graphs are generated efficiently with low computational complexity. Extensive experimental results on four benchmarks demonstrate the superiority of the proposed approach over the state-of-the-art methods.

Cross-media hashing, which maps data from different modalities to a low-dimensional sharing Hamming space, has attracted considerable attention due to the rapid increase of multimodal data, for example, images and texts. Recent cross-media hashing works mainly aim at learning compact hash codes to preserve the class label-based or feature-based similarities among samples. However, these methods ignore the unbalanced semantic gaps between different modalities and high-level semantic concepts, which generally results in less effective hash functions and unsatisfying retrieval performance. Specifically, the key words of texts contain semantic meanings, while the low-level features of images lack of semantic meanings. That means the semantic gap in image modality is larger than that in text modality. In this paper, we propose a simple yet effective hashing method for cross-media retrieval to address this problem, dubbed discrete semantic alignment hashing (DSAH). First, DSAH formulates to exploit collaborative filtering to mine the relations between class labels and hash codes, which can reduce memory consumption and computational cost compared to pairwise similarity. Then, the attribute of image modality is employed to align the semantic information with text modality. Finally, to further improve the quality of hash codes, we propose a discrete optimization algorithm to learn discrete hash codes directly, and each bit has a closed-form solution. Extensive experiments on multiple public databases show that our model can seamlessly incorporate attributes and achieve promising performance.

Cross-modal retrieval has become a hot research topic in recent years for its theoretical and practical significance. This paper proposes a new technique for learning such deep visual-semantic embedding that is more effective and interpretable for cross-modal retrieval. The proposed method employs a two-stage strategy to fulfill the task. In the first stage, deep mutual information estimation is incorporated into the objective to maximize the mutual information between the input data and its embedding. In the second stage, an expelling branch is added to the network to disentangle the modality-exclusive information from the learned representations. This helps to reduce the impact of modality-exclusive information to the common subspace representation as well as improve the interpretability of the learned feature. Extensive experiments on two large-scale benchmark datasets demonstrate that our method can learn better visual-semantic embedding and achieve state-of-the-art cross-modal retrieval results.

Semantic image segmentation can be accomplished by assigning a proper object category label to each meaningful region of an image. Beyond the original bottom-up models, the use of top-down categorization information has been applied to semantic segmentation to improve performance. An excellent example of such a top-down scheme is to integrate a Conditional Random Field (CRF) model with sparse dictionary learning. However, the existing solutions merely consider the discrimination of dictionaries to obtain better sparse codes, without considering the inherent data locality characteristics. In this paper, we explore such characteristics and propose a novel semantic segmentation framework based on an innovative CRF model with locality-consistent dictionary learning. In particular, we propose two new locality-consistent dictionary learning strategies by capturing the local consistencies in the feature space and the label space. In addition, we develop a joint dictionary and a CRF model parameter learning algorithm to seamlessly integrate the proposed locality-consistent dictionary learning strategies into the CRF model. Extensive experiments are conducted with two popular databases of different traits (i.e., Graz-02 and PASCAL-CONTEXT). The simulation results confirm the efficiency of the proposed scheme, especially when training data are limited.

In realistic steganalysis applications, the mismatched problem can lead to the degradation of performance in steganalysis. The main reason is the discrepancy of feature distributions between training set and testing set. In this paper, we present a Contribution-based Feature Transfer (CFT) algorithm for JPEG mismatched steganalysis. CFT tries to learn two transformations to transfer training set features by evaluating both the sample feature and dimensional feature contributions. We can obtain new feature representations so as to approach the feature distribution of the testing samples. The comparison to prior arts reveals the superiority of CFT on the experiments for the mismatched JPEG steganalysis in the heterogeneous cover source scenario.

Saliency detection has been long researched. However, most existing algorithms can not uniformly highlight salient objects. To approach this problem, we propose a novel saliency detection algorithm based on the Local Single Gaussian Model (LSGM). First, we utilize a bottom-up model to generate an initial saliency map and construct a background dictionary and a foreground dictionary based on the initial saliency map, respectively. Then, a LSGM is used to obtain a LSGM-based map. Note that we construct a corresponding LSGM for each superpixel region and thus the LSGM is a dynamic model with geometric structure information. Finally, we integrate the LSGM-based saliency map and the initial bottom-up map with global information as the final saliency map. Extensive experiments on four public datasets show that our algorithm outperforms state-of-the-art methods.

Unsupervised feature selection has shown significant potential in distance-based clustering tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss function is introduced to enforce the selected feature groups to preserve ordinal locality of original data, which contributes to distance-based clustering tasks. Secondly, we simplify the orthogonal basis clustering by imposing an orthogonal constraint on the feature projection matrix. Consequently, a general framework for simultaneous feature selection and clustering is discussed. Thirdly, an alternating minimization algorithm is employed to efficiently optimize the proposed model together with rapid convergence. Extensive comparison experiments on several benchmark datasets well validate the encouraging gain in clustering from our proposed method.

In this letter, we propose a novel classification strategy called the coupled dictionary learning for target recognition in synthetic aperture radar (SAR) images. First, we train structured synthesis dictionaries to reflect the difference among each category. Second, we introduce a shared dictionary to reduce the effect of common features, such as the high similarity caused by specular reflection. Finally, we use the analysis dictionary to improve the efficiency of recognition by eliminating the constraint of the l0-norm or l1-norm of sparse code. Experimental results on Moving and Stationary Target Acquisition and Recognition data set indicate that our method can achieve better performance in SAR target recognition than the state-of-the-art methods, such as tritask joint sparse representation and CKLR. Especially, this method can be more robust when the depressions have obvious changes.

Dictionary learning (DL) plays an important role in pattern classification. However, learning a discriminative dictionary has not been well addressed in analysis dictionary learning (ADL). This letter proposes a Class-aware Analysis Dictionary Learning (CADL) model to improve the classification performance of conventional ADL. The objective function of CADL mainly includes two parts to promote the discriminability. The first part aims to learn a discriminative analysis subdictionary for each class instead of a global dictionary for all classes. The learned analysis dictionary is class-aware, generating a block-diagonal coding coefficient matrix. The second part aims to enhance the discrimination of coding coefficients by integrating a max-margin regularization term into our proposed framework. This term ensures the coefficients of different classes to be separated by a max-margin, which boosts the confidence of classification. A theoretical analysis is also given to support the max-margin regularization term from the perspective of preserving the pairwise relations of samples in coding space. We employ an alternating minimization algorithm to iteratively find the convergent solution. By evaluating our method on four pattern classification datasets, we demonstrate the superiority of our CADL method to the state-of-the-art DL methods.

Large-scale is a trend in person re-identi- fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets.

Dictionary learning (DL) has been successfully applied to various pattern classification tasks in recent years. However, analysis dictionary learning (ADL), as a major branch of DL, has not yet been fully exploited in classification due to its poor discriminability. This paper presents a novel DL method, namely Discriminative Analysis Dictionary Learning (DADL), to improve the classification performance of ADL. First, a code consistent term is integrated into the basic analysis model to improve discriminability. Second, a triplet constraint-based local topology preserving loss function is introduced to capture the discriminative geometrical structures embedded in data. Third, correntropy induced metric is employed as a robust measure to better control outliers for classification. Then, half-quadratic minimization and alternate search strategy are used to speed up the optimization process so that there exist closed-form solutions in each alternating minimization stage. Experiments on several commonly used databases show that our proposed method not only significantly improves the discriminative ability of ADL, but also outperforms state-of-the-art synthesis DL methods.

In recent years, dictionary learning (DL) has shown significant potential in various classification tasks. However, most of previous works aim to learn a synthesis dictionary. The other major category of DL-analysis dictionary learning has not been fully exploited yet. This paper proposes a novel DL method, named Topology Preserving Dictionary Learning (TPDL). First, we propose a triplet-constraint-based topology preserving loss function to capture the underlying local topological structures of data in a supervised manner. Second, a sparse-label-matrix-based function is integrated into the basic analysis model to improve discriminative ability. Third, Huber M-estimator is employed as a robust metric to handle the errors (e.g., outliers and noise) that possibly exist in data. Then, an alternating optimization algorithm is developed based on half-quadratic minimization and alternate search strategy. Closed-form solutions in each alternating optimization stage speed up the minimization process. Experiments on four commonly used datasets show that our proposed TPDL achieves competitive performance in contrast to state-of-the-art DL methods.

Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.

Relative attributes learning provides a way to capture the strength of the attributes under consideration and it can provide a more specific and accurate information to describe images. But for computers, extracting low-level features is the foundation of understanding images. Thus there is no doubt that the features will have an significant influence on relative attribute models learning. %For example, local features don't have much positive effects on learning global attributes. In this paper, we propose a sparse feature preservation (SFP) method to preserve the most important features on the learning of each attribute model. SFP is formulated through using rearrangement inequality according to relative attribute models learning. We first train the relative attribute models according to the supervision information of attribute pairs. Then the sorting results are used to train the key feature preservation factors and the sparse features are utilized to retrain the relative attribute models. We demonstrate the approach on five datasets and show its significant improvement on the accuracy of relative attribute learning.

The Convolutional Neural Networks (CNNs) have achieved breakthroughs on several image retrieval benchmarks. Most previous works re-formulate CNNs as global feature extractors used for linear scan. This paper proposes a Multi-layer Orderless Fusion (MOF) approach to integrate the activations of CNN in the Bag-of-Words (BoW) framework. Specifically, through only one forward pass in the network, we extract multi-layer CNN activations of local patches. Activations from each layer are aggregated in one BoW model, and several BoW models are combined with late fusion. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method.

Cross-modal hashing has attracted considerable attention due to its low storage cost and fast retrieval speed. Recently, more and more sophisticated researches related to this topic are proposed. However, they seem to be inefficient computationally for several reasons. On one hand, learning coupled hash projections makes the iterative optimization problem challenging. On the other hand, individual collective binary codes for each content are also learned with a high computation complexity. In this paper we describe a simple yet effective cross-modal hashing approach that can be implemented in just three lines of code. This approach first obtains the binary codes for one modality via unimodal hashing methods (e.g., iterative quantization (ITQ)), then applies simple linear regression to project the other modalities into the obtained binary subspace. Obviously, it is non-iterative and parameter-free, which makes it more attractive for many real-world applications. We further compare our approach with other state-of-the-art methods on four benchmark datasets (i.e., the Wiki, VOC, LabelMe and NUS-WIDE datasets). Despite its extraordinary simplicity, our approach performs remarkably and generally well for these datasets under different experimental settings (i.e., large-scale, high-dimensional and multi-label datasets).

In this study, the authors consider additive spread‐spectrum (SS) data embedding in transform‐domain host data. Conventional additive SS embedding schemes use an equal‐amplitude modulated carrier to deposit one information symbol across a group of host data coefficients which act as interference to SS signal of interest. If there is a flexibility of assigning different amplitudes across symbol bits, the probability of error can be further reduced by adaptively allocating amplitude to each symbol bit based on its own host/interference. In this study, they present a novel amplitude‐adaptive SS embedding scheme. Particularly, symbol‐by‐symbol adaptive amplitude allocation algorithms are developed to compensate for the impact from the known interference. They aim at designing the SS embedding amplitude for each symbol adaptively in order to minimise the receiver bit‐error‐rate (BER) at any given distortion level. Then, optimised amplitude allocation for multi‐carrier/multi‐message embedding in the same host data is studied as well. Finally, they consider the problem of amplitude optimisation for an ideal scenario where no external noise is introduced during embedding and transmission. Extensive experimental results illustrate that the proposed amplitude‐adaptive SS embedding scheme can provide order‐of‐magnitude performance improvement over several other state‐of‐the‐art SS embedding schemes.

Fine-grained visual categorization (FGVC) refers to the task of classifying objects that belong to the same basic-level class (e.g., different bird species). Since the subtle inter-class variation often exists on small parts (e.g., beak, belly, etc.), it is reasonable to localize semantic parts of an object before describing it. However, unsupervised part-segmentation methods often suffer from over-segmentation which harms the quality of image representation. In this paper, we present a fine-tuning approach to tackle this problem. To this end, we perform a greedy algorithm to optimize an intuitive objective function, preserving principal parts meanwhile filtering noises, and further construct mid-level parts beyond the refined parts toward a more descriptive representation. Experiments demonstrate that our approach achieves competitive classification accuracy on the CUB-200-2011 dataset with both Fisher vectors and deep conv-net features.

Feature extraction is a crucial and difficult issue in pattern recognition tasks with the high-dimensional and multiple features. To extract the latent structure of multiple features without label information, multi-view learning algorithms have been developed. In this paper, motivated by manifold learning and multi-view Non-negative Matrix Factorization (NM-F), we introduce a novel feature extraction method via multi-view NMF with local graph regularization, where the inner-view relatedness between data is taken into consideration. We propose the matrix factorization objective function by constructing a nearest neighbor graph to integrate local geometrical information of each view and apply two iterative updating rules to effectively solve the optimization problem. In the experiment, we use the extracted feature to cluster several realistic datasets. The experimental results demonstrate the effectiveness of our proposed feature extraction approach.

Relative attributes provide accurate information for image processing to describe which image is more natural, more open, etc. Robustness of relative attribute learning depends on the labeled comparative image pairs. However, manually labeling is a labor intensive and time-consuming task. In this paper, a semi-supervised learning approach based on group sparse is proposed to discover pairwise comparisons automatically. We generate an initial level division of the labeled training images for the basic of new constraints. Then, group sparse representation for the unlabeled images is introduced by embedding the level information into the dictionary. The semi-supervised process is conducted by selecting samples which have minimum reconstruction errors and adding new constraints to the model by comparing the selected ones with the samples in dictionary. Experiments on three public datasets demonstrate the effectiveness of our proposed method.

The explosive growth in touchscreen smartphones and tablets need simpler and intelligent image retrieval method to offer the convenience for users. Sketch based image retrieval (SBIR) which is based on a free hand sketch has recently attracted more attention, but current methods are sensitive to rotation, scaling and translation (RST). In this paper, we introduce an efficient SBIR method called RST-Invariant Circular Description (RICD). The proposed method utilizes saliency map and boundary information to detect salient contours for each image, then uses patch hashing to eliminate the deformations and redundancies of sketches. To achieve rotational invariance, we describe salient contours using circular description. The experiment results on two public datasets demonstrated that the proposed method outperforms the state-of-the-arts in both natural and product images, and handles rotation, scaling and translation transformations.

This paper considers the problem of intelligent jamming attack on a MIMO wireless communication link with a transmitter, a receiver, and an adversarial jammer, each equipped with multiple antennas. We present an optimal jamming signal design, which can maximally disrupt the MIMO transmission when the transceiver adopts an anti-jamming mechanism. In particular, signal-to-jamming-plus-noise ratio (SJNR) at the receiver is used as the anti-jamming reliability metric of the legitimate MIMO transmission. The jamming signal design is developed under the most crucial scenario for the jammer where the legitimate transceiver adopt jointly designed maximum-SJNR transmit beamforming and receive filter to suppress/mitigate the disturbance from the jammer. Under this best anti-jamming scheme, we aim to optimize the jamming signal to minimize the receiver's maximum-SJNR under a given jamming power budget. The optimal jamming signal designs are developed in different cases with accordance to the availability of channel state information (CSI) at the jammer. The analytical approximations of the jamming performance in terms of average maximum-SJNR are also provided. Extensive simulation studies confirm our analytical predictions and illustrate the efficiency of the designed optimal jamming signal on disrupting MIMO communications.

We present a semi-automatic method to reconstruct shredded documents. The novelty of the method lies in the way it performs pairwise matching of chads. The technique divides chad contours into curves using corner detection and introduces a procedure to assess the match of two curves. The proposed curve matching technique is robust to translation and rotation and can cope with shape deformations due to shredding by allowing overlapping of chads during matching. The alignment of text lines, crossing characters and color information on the chads is also utilized to improve matching performance. Visual interfaces are designed to allow for user input in identifying correctly matching chad pairs and reconstructing the document. The effectiveness of the method is demonstrated by solving the first and second puzzles of the DARPA shredder challenge.

Due to its compact binary codes and efficient search scheme, image hashing method is suitable for large-scale image retrieval. In image hashing methods, Hamming distance is used to measure similarity between two points. For K-bit binary codes, the Hamming distance is an into and bounded by K. Therefore, there are many returned images share the same Hamming distances with the query. In this paper, we propose an efficient image ranking method based on bit importance of binary code. Compared with the returned images of Hamming distance, important bits of query image are detected. Then, large weights are assigned to important bits and small weights are assigned to minor bits. The advantage of this proposed method is calculation efficiency. Evaluations on two large-scale image data sets demonstrate the efficacy of our binary code ranking method based on bit importance.

Attribute based image retrieval has offered a powerful way to bridge the gap between low level features and high level semantic concepts. However, existing methods rely on manually pre-labeled queries, limiting their scalability and discriminative power. Moreover, such retrieval systems restrict the users to use only the exact pre-defined query words when describing the intended search targets, and thus fail to offer good user experience. In this paper, we propose a principled approach to automatically enrich the attribute representation by leveraging additional linguistic knowledge. To this end, an external semantic pool is introduced into the learning paradigm. In addition to modelling the relations between attributes and low level features, we also model the join interdependencies of pre-labeled attributes and semantically extended attributes, which is more expressive and flexible. We further propose a novel semantic relation measure for extended attribute learning in order to take user preference into account, which we see as a step towards practical systems. Extensive experiments on several attribute benchmarks show that our approach outperforms several state-of-the-art methods and achieves promising results in improving user experience.

Most universal JPEG steganalysis approaches rely on the assumption that training and testing samples come from the same distribution. They fail when training set and testing set are mismatched. In this paper, we propose generalized transfer component analysis for mismatched JPEG steganalysis to derive new representations from original features for training and testing samples to correct the mismatches. We first apply domain alignment to transform source domain (training set) to an intermediate domain closer to target domain (testing set). Then a set of common transfer components are learnt across two domains by minimizing the distribution distance between them. In the space spanned by these transfer components, two domains manifest similar characteristics and preserve enough discrimination to different categories. Extensive experiments demonstrate our method performs well in mismatched JPEG steganalysis.

This paper proposes a novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. In the first stage, we propose a general multisubspace framework to learn a robust metric in which noise and outliers in image pixels are detected. Potential loss functions, including L1 , L2,1, and correntropy are studied. In the second stage, based on the learned metric and collaborative representation, we propose an efficient nonnegative sparse representation algorithm to find an approximation solution of sparse representation. According to the L1 ball theory in sparse representation, the approximated solution is unique and can be optimized efficiently. Then a filtering strategy is developed to avoid the computation of the sparse representation on the whole large-scale dataset. Moreover, theoretical analysis also gives the necessary condition for nonnegative least squares technique to find a sparse solution. Extensive experiments on several public databases have demonstrated that the proposed TSR approach, in general, achieves better classification accuracy than the state-of-the-art sparse representation methods. More importantly, a significant reduction of computational costs is reached in comparison with sparse representation classifier; this enables the TSR to be more suitable for robust face recognition on a large-scale dataset.

Cell-phones have become a necessary communication accessory in daily life. MMS (Multimedia Messaging Service) used by smart phones has caused higher requirement on mobile image manipulation. Classifying image source cell-phones has become a major issue in the cell-phone communication forensics. There are two ways usually used for tracing and identifying the source device: image characteristics and equipment fingerprint. Both of the above schemes require a set of images captured by known source cell-phones for training a classification model. To avoid using any prior knowledge in practical scenarios, a graph based approach was proposed to classify the source cell-phones. Though an acceptable result has been obtained, a problem of incomplete classification appears in the case that one image is classified wrong into a single subset. In this paper, a silhouette coefficient based algorithm is proposed for source cell-phone classification. The spectral clustering algorithm is adopted in graph partitioning and the silhouette coefficient is used to extract the optimal classification from all the possibilities of classification. Experimental results show the validity of the proposed method.

An informative and discriminative graph plays an important role in the graph-based semi-supervised learning methods. This paper introduces a nonnegative sparse algorithm and its approximated algorithm based on the l0-l1equivalence theory to compute the nonnegative sparse weights of a graph. Hence, the sparse probability graph (SPG) is termed for representing the proposed method. The nonnegative sparse weights in the graph naturally serve as clustering indicators, benefiting for semi-supervised learning. More important, our approximation algorithm speeds up the computation of the nonnegative sparse coding, which is still a bottle-neck for any previous attempts of sparse non-negative graph learning. And it is much more efficient than using l1-norm sparsity technique for learning large scale sparse graph. Finally, for discriminative semi-supervised learning, an adaptive label propagation algorithm is also proposed to iteratively predict the labels of data on the SPG. Promising experimental results show that the nonnegative sparse coding is efficient and effective for discriminative semi-supervised learning.

Double compression usually occurs after the image has gone through some kinds of tampering, so double compression detection is a basic mean to assess the authenticity of a given image. In this paper, we propose to model the distribution of the mode based first digits of DCT (Discrete Cosine Transform) coefficients using Markov transition probability matrix and utilize its stationary distribution as features for double compression detection. Experiment results show the effectiveness of the proposed method and comparison has been made to show the improvement by using this second order statistical model.

Hashing methods have attracted much attention in large scale image research in recent years, because they are not only fast, but also needing a little memory. This paper proposed a balanced semi-supervised hashing method by dividing image into several blocks. With the help of improved semi-supervised hashing, we obtain a short hash code of each block, which jointed together forms a hash code of an integrated image. In the improved semi-supervised hashing, the supervised information is completed by combining the similarity of image pairs and label information. Extensive experiments demonstrate that our method can get more balanced result between retrieval speed, saving storage of original data and retrieval accuracy in CBIR than the state-of-the-art hashing methods.

This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers. Then an l1regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.

Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L(1) norm when outliers occur.

A printed document can provide intrinsic features of the printer so as to distinguish which printer it comes from. But how to extract the intrinsic features is critical in printer forensics. In this paper, the page document's geometric distortion is extracted as the intrinsic features, and a printer forensics method based on the distortion is proposed. Firstly projective transformation model is used to model the geometric distortion. After the feature point set of the model is extracted, the model's parameters considered as the geometric distortion features can be estimated, and then the model's error pattern can be obtained. During the process, the least squares method is used to estimate the model's parameters, and SVM technique is used for classification. The effectiveness of the model's parameters in the printer forensics is demonstrated by experimental results.