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The development of integrated avionics systems and electromagnetic spectrum technology has attracted widespread attention. It has further increased the performance requirements for modulation signal recognition technology in complex electromagnetic environments. Therefore, this paper proposes a deep joint learning technique, including deep representation and low-dimensionality discrimination, to enhance feature stability and environmental adaptability. Specifically, we design a feature learning network based on AlexNet to extract in-depth features and optimize it through parameter-based transfer learning techniques, promote multi-level representation capabilities of features and reduce the sample size requirements. Moreover, we propose a classification algorithm based on kernel collaborative representation and discriminative projection to enhance the ability of low-dimensionality representation and between-class discrimination, which optimized using the mini-batch random gradient descent method. As shown in the simulation, the overall average recognition success rate of this method aiming at twelve radar signal modulation types reaches 97.58% at SNR of -6dB. The results of simulation and analysis demonstrate the superiority of the proposed model in terms of robustness, timeliness, and adaptability to small samples.

Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method. [ABSTRACT FROM AUTHOR]

The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and representation-based classification (DPRC) method to enhance the discriminant ability of the SRC. The proposed method first obtains a discriminative projection matrix not only maximizing the ratio of the distance within interclass over the distance within intraclass, but also minimizing the linear approximation error within intraclass. Then it maps the original data onto the discriminative space, and adopts an SRC method to obtain the final solution. An inexact augmented Lagrangian method of multiplier is proposed for finding the optimal representation vector in our framework, and a proximal alternating minimization method is adopted to the iteration subproblems of the proposed method. The proposed method is proven to have the subsequence convergence property. Experimental results on Yale, ORL, and AR face image databases demonstrate that, compared with some existing feature extraction methods based on the SRC, the proposed DPRC method is more efficient. [ABSTRACT FROM AUTHOR]

Because of the simplicity and effectiveness of linear regression classification (LRC), LRC is widely applied into image classification. However, it processes the original high-dimensional data directly. It is well known that the original data usually contains a lot of redundant information or noise, which will reduce the performance of LRC algorithm and increase its running cost. At the same time, it usually suffers from out of sample problem. In order to overcome the weaknesses of LRC, a novel dimension reduction algorithm termed linear regression classification steered discriminative projection (LRC-DP) is presented by combining LRC with discriminative projection. LRC-DP not only fits LRC well, but also seeks a linear projection, in which the ratio of between-class reconstruction errors to within-class reconstruction errors is maximized in the transformation space. The proposed LRC-DP can learn a robust low-dimensional projection subspace from the original sample images in high-dimension space. In order to validate the performance of LRC-DP algorithm, extensive experiments are conducted on several public image databases. Experimental results reveal that the LRC-DP algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]

Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question. In this paper, we present a novel discriminative projection learning approach with the objective of seeking a projection matrix such that the learned low-dimensional representation can fit SRC well and that it has well discriminant ability. More specifically, we formulate the learning algorithm as a bilevel optimization problem, where the optimization includes an $\ell _{1}$ -norm minimization problem in its constraints. Through the bilevel optimization model, the relationship between sparse representation and the desired feature projection can be explicitly exploited during the learning process. Therefore, SRC can achieve a better performance in the transformed subspace. The optimization model can be solved by using a stochastic gradient ascent algorithm, and the desired gradient is computed using implicit differentiation. Furthermore, our method can be easily extended to learn a dictionary. The extensive experimental results on a series of benchmark databases show that our method outperforms many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Along with the rapid development of computer and image processing technology, it is definitely convenient to obtain various images for subjects, which can be more robust to classification as more feature information is contained. However, how to effectively exploit the rich discriminative information within image sets is the key problem. In this paper, based on the concept of dual linear regression classification method for image set classification, we propose a novel discriminative framework to exploit the superiority of discriminant regression mechanism. We aim to learn a projection matrix to force the represented image points from the same class to be close and those from different class are better separated. The feature extraction strategy in our discriminative framework can appropriately work with the corresponding classification strategy, thus, better classification performance can be achieved. Moreover, we propose a kernel discriminative extension method to address the non-linearity problem by adopting the kernel trick. From the experimental results, our proposed method can obtain competitive recognition rates on face recognition tasks via mapping the original image sets into a more discriminative feature space. Besides, it also shows the effectiveness for object classification task with small image sizes and different number of frames. [ABSTRACT FROM AUTHOR]

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Remote person identification by gait is one of the most important topics in the field of computer vision and pattern recognition. However, gait recognition suffers severely from the appearance variance caused by the view change. It is very common that gait recognition has a high performance when the view is fixed but the performance will have a sharp decrease when the view variance becomes significant. Existing approaches have tried all kinds of strategies like tensor analysis or view transform models to slow down the trend of performance decrease but still have potential for further improvement. In this paper, a discriminative projection with list-wise constraints (DPLC) is proposed to deal with view variance in cross-view gait recognition, which has been further refined by introducing a rectification term to automatically capture the principal discriminative information. The DPLC with rectification (DPLCR) embeds list-wise relative similarity measurement among intraclass and inner-class individuals, which can learn a more discriminative and robust projection. Based on the original DPLCR, we have introduced the kernel trick to exploit nonlinear cross-view correlations and extended DPLCR to deal with the problem of multiview gait recognition. Moreover, a simple yet efficient gait representation, namely gait individuality image (GII), based on gait energy image is proposed, which could better capture the discriminative information for cross view gait recognition. Experiments have been conducted in the CASIA-B database and the experimental results demonstrate the outstanding performance of both the DPLCR framework and the new GII representation. It is shown that the DPLCR-based cross-view gait recognition has outperformed the-state-of-the-art approaches in almost all cases under large view variance. The combination of the GII representation and the DPLCR has further enhanced the performance to be a new benchmark for cross-view gait recognition. [ABSTRACT FROM AUTHOR]

Accurate, rapid and non-destructive disease identification in the early stage of infection is essential to ensure the safe and efficient production of greenhouse cucumbers. Nevertheless, the effectiveness of most existing methods relies on the disease already exhibiting obvious symptoms in the middle to late stages of infection. Therefore, this paper presents an early identification method for cucumber diseases based on the techniques of hyperspectral imaging and machine learning, which consists of two procedures. First, reconstruction fidelity terms and graph constraints are constructed based on the decision criterion of the collaborative representation classifier and the desired spatial distribution of spectral curves (391 to 1044 nm) respectively. The former constrains the same-class and different-class reconstruction residuals while the latter constrains the weighted distances between spectral curves. They are further fused to steer the design of an offline algorithm. The algorithm aims to train a linear discriminative projection to transform the original spectral curves into a low dimensional space, where the projected spectral curves of different diseases own better separation trends. Then, the collaborative representation classifier is utilized to achieve online early diagnosis. Five experiments were performed on the hyperspectral data collected in the early infection stage of cucumber anthracnose and Corynespora cassiicola diseases. Experimental results demonstrated that the proposed method was feasible and effective, providing a maximal identification accuracy of 98.2% and an average online identification time of 0.65 ms. The proposed method has a promising future in practical production due to its high diagnostic accuracy and short diagnosis time.

Classification is often performed after feature extraction. To improve the recognition performance, we could develop the optimal feature extraction method for a classification method. In this paper, we propose three feature extraction methods Discriminative Projection for Nearest Neighbor (DP-NN), Discriminative Projection for Nearest Mean (DP-NM) and Discriminative Projection for Nearest Feature Line (DP-NFL), which are optimal for classification methods Nearest Neighbor (NN), Nearest Mean (NM) and Nearest Feature Line (NFL), respectively. We also prove that DP-NN and DP-NM are equivalent to Linear Discriminant Analysis (LDA) under a certain condition. In the experiments, LDA, DP-NFL and SRC steered Discriminative Projection (SRC-DP) are used for feature extraction and then the extracted features are classified by NN, NM, NFL, Sparse Representation based Classification (SRC) and Collaborative Representation Classifier (CRC). Experimental results of biometric recognition show that the proposed DP-NFL performs well, and that combining an effective classification method with the optimal feature extraction method for it can perform best. [ABSTRACT FROM AUTHOR]

Computational paralinguistics deals with underlying meaning of the verbal messages, which is of interest in manifold applications ranging from intelligent tutoring systems to affect sensitive robots. The state-of-the-art pipeline of paralinguistic speech analysis utilizes brute-force feature extraction, and the features need to be tailored according to the relevant task. In this work, we extend a recent discriminative projection based feature selection method using the power of stochasticity to overcome local minima and to reduce the computational complexity. The proposed approach assigns weights both to groups and to features individually in many randomly selected contexts and then combines them for a final ranking. The efficacy of the proposed method is shown in a recent paralinguistic challenge corpus to detect level of conflict in dyadic and group conversations. We advance the state-of-the-art in this corpus using the INTERSPEECH 2013 Challenge protocol. [ABSTRACT FROM AUTHOR]

Accurate, rapid and non-destructive disease identification in the early stage of infection is essential to ensure the safe and efficient production of greenhouse cucumbers. Nevertheless, the effectiveness of most existing methods relies on the disease already exhibiting obvious symptoms in the middle to late stages of infection. Therefore, this paper presents an early identification method for cucumber diseases based on the techniques of hyperspectral imaging and machine learning, which consists of two procedures. First, reconstruction fidelity terms and graph constraints are constructed based on the decision criterion of the collaborative representation classifier and the desired spatial distribution of spectral curves (391 to 1044 nm) respectively. The former constrains the same-class and different-class reconstruction residuals while the latter constrains the weighted distances between spectral curves. They are further fused to steer the design of an offline algorithm. The algorithm aims to train a linear discriminative projection to transform the original spectral curves into a low dimensional space, where the projected spectral curves of different diseases own better separation trends. Then, the collaborative representation classifier is utilized to achieve online early diagnosis. Five experiments were performed on the hyperspectral data collected in the early infection stage of cucumber anthracnose and Corynespora cassiicola diseases. Experimental results demonstrated that the proposed method was feasible and effective, providing a maximal identification accuracy of 98.2% and an average online identification time of 0.65 ms. The proposed method has a promising future in practical production due to its high diagnostic accuracy and short diagnosis time. [ABSTRACT FROM AUTHOR]